{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification with Pre-trained Imagenet CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example is adapted from [Caffe's iPython Notebook example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). We use pre-trained CNN model [converted](http://mochajl.readthedocs.org/en/latest/user-guide/tools/import-caffe-model.html) from Caffe's [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). Specifically, we use `bvlc_reference_caffenet`, which is the same model used in Caffe's own iPython Notebook example.\n",
    "\n",
    "This notebook is located in `examples/ijulia/ilsvrc12` under Mocha's source directory. If you want to run this example by yourself, you need to\n",
    "\n",
    "* Install [IJulia.jl](https://github.com/JuliaLang/IJulia.jl), which is a Julia backend for IPython. Of course, you also need to have Python and IPython installed.\n",
    "* Install [Images.jl](https://github.com/timholy/Images.jl), which we will use to read image files in this example.\n",
    "* (Optional) Install [Gadfly.jl](http://gadflyjl.org/), which will be used to plot the class probability prediction.\n",
    "* Download pre-trained CNN model. There is a shell script `get-model.sh` that you could run to download the pre-trained CNN model in HDF5 format converted from Caffe's original binary protocol buffer format.\n",
    "\n",
    "After all the preparation, you can start the notebook by executing the following command in this demo's source directory.\n",
    "\n",
    "```bash\n",
    "jupyter notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructing Convolutional Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use Mocha's native extension here to get faster convolution. If you prefer to disable it or use CUDA backend instead, please refer to [Mocha's document](http://mochajl.readthedocs.org/en/latest/user-guide/backend.html) for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Configuring Mocha...\n",
      " * CUDA       disabled by default\n",
      " * Native Ext disabled by default\n",
      "Mocha configured, continue loading module...\n",
      "DefaultBackend = Mocha.CPUBackend\n"
     ]
    }
   ],
   "source": [
    "#ENV[\"MOCHA_USE_NATIVE_EXT\"] = \"true\"\n",
    "\n",
    "using Mocha\n",
    "\n",
    "backend = CPUBackend()\n",
    "init(backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will define the network structure. This is directly adapted from Caffe's `bvlc_reference_caffenet` model definition. Please refer to Mocha's CIFAR-10 tutorial on how to translate Caffe's model definition to Mocha. This model takes 3-channel color images of size 256-by-256 and crop to take a 227-by-227 region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2018-06-27T13:57:34 | info | Mocha]: Constructing net imagenet on Mocha.CPUBackend...\n",
      "[2018-06-27T13:57:35 | info | Mocha]: Topological sorting 16 layers...\n",
      "[2018-06-27T13:57:35 | info | Mocha]: Setup layers...\n",
      "[2018-06-27T13:57:37 | info | Mocha]: Network constructed!\n",
      "************************************************************\n",
      "          NAME: imagenet\n",
      "       BACKEND: Mocha.CPUBackend\n",
      "  ARCHITECTURE: 16 layers\n",
      "............................................................\n",
      " *** Mocha.MemoryDataLayer(data)\n",
      "    Outputs ---------------------------\n",
      "          data: Blob(256 x 256 x 3 x 1)\n",
      "............................................................\n",
      " *** Mocha.CropLayer(crop)\n",
      "    Inputs ----------------------------\n",
      "          data: Blob(256 x 256 x 3 x 1)\n",
      "    Outputs ---------------------------\n",
      "       cropped: Blob(227 x 227 x 3 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv1)\n",
      "    Inputs ----------------------------\n",
      "       cropped: Blob(227 x 227 x 3 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv1: Blob(55 x 55 x 96 x 1)\n",
      "............................................................\n",
      " *** Mocha.PoolingLayer(pool1)\n",
      "    Inputs ----------------------------\n",
      "         conv1: Blob(55 x 55 x 96 x 1)\n",
      "    Outputs ---------------------------\n",
      "         pool1: Blob(27 x 27 x 96 x 1)\n",
      "............................................................\n",
      " *** Mocha.LRNLayer(norm1)\n",
      "    Inputs ----------------------------\n",
      "         pool1: Blob(27 x 27 x 96 x 1)\n",
      "    Outputs ---------------------------\n",
      "         norm1: Blob(27 x 27 x 96 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv2)\n",
      "    Inputs ----------------------------\n",
      "         norm1: Blob(27 x 27 x 96 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv2: Blob(27 x 27 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.PoolingLayer(pool2)\n",
      "    Inputs ----------------------------\n",
      "         conv2: Blob(27 x 27 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         pool2: Blob(13 x 13 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.LRNLayer(norm2)\n",
      "    Inputs ----------------------------\n",
      "         pool2: Blob(13 x 13 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         norm2: Blob(13 x 13 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv3)\n",
      "    Inputs ----------------------------\n",
      "         norm2: Blob(13 x 13 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv3: Blob(13 x 13 x 384 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv4)\n",
      "    Inputs ----------------------------\n",
      "         conv3: Blob(13 x 13 x 384 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv4: Blob(13 x 13 x 384 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv5)\n",
      "    Inputs ----------------------------\n",
      "         conv4: Blob(13 x 13 x 384 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv5: Blob(13 x 13 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.PoolingLayer(pool5)\n",
      "    Inputs ----------------------------\n",
      "         conv5: Blob(13 x 13 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         pool5: Blob(6 x 6 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.InnerProductLayer(fc6)\n",
      "    Inputs ----------------------------\n",
      "         pool5: Blob(6 x 6 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "           fc6: Blob(4096 x 1)\n",
      "............................................................\n",
      " *** Mocha.InnerProductLayer(fc7)\n",
      "    Inputs ----------------------------\n",
      "           fc6: Blob(4096 x 1)\n",
      "    Outputs ---------------------------\n",
      "           fc7: Blob(4096 x 1)\n",
      "............................................................\n",
      " *** Mocha.InnerProductLayer(fc8)\n",
      "    Inputs ----------------------------\n",
      "           fc7: Blob(4096 x 1)\n",
      "    Outputs ---------------------------\n",
      "           fc8: Blob(1000 x 1)\n",
      "............................................................\n",
      " *** Mocha.SoftmaxLayer(prob)\n",
      "    Inputs ----------------------------\n",
      "           fc8: Blob(1000 x 1)\n",
      "    Outputs ---------------------------\n",
      "          prob: Blob(1000 x 1)\n",
      "************************************************************\n",
      "\n"
     ]
    }
   ],
   "source": [
    "img_width, img_height, img_channels = (256, 256, 3)\n",
    "crop_size = (227, 227)\n",
    "batch_size = 1  # could be larger if you want to classify a bunch of images at a time\n",
    "\n",
    "layers = [\n",
    "  MemoryDataLayer(name=\"data\", tops=[:data], batch_size=batch_size,\n",
    "      transformers=[(:data, DataTransformers.Scale(scale=255)),\n",
    "                    (:data, DataTransformers.SubMean(mean_file=\"model/ilsvrc12_mean.hdf5\"))],\n",
    "      data = Array[zeros(img_width, img_height, img_channels, batch_size)])\n",
    "  CropLayer(name=\"crop\", tops=[:cropped], bottoms=[:data], crop_size=crop_size)\n",
    "  ConvolutionLayer(name=\"conv1\", tops=[:conv1], bottoms=[:cropped],\n",
    "      kernel=(11,11), stride=(4,4), n_filter=96, neuron=Neurons.ReLU())\n",
    "  PoolingLayer(name=\"pool1\", tops=[:pool1], bottoms=[:conv1],\n",
    "      kernel=(3,3), stride=(2,2), pooling=Pooling.Max())\n",
    "  LRNLayer(name=\"norm1\", tops=[:norm1], bottoms=[:pool1],\n",
    "      kernel=5, scale=0.0001, power=0.75)\n",
    "  ConvolutionLayer(name=\"conv2\", tops=[:conv2], bottoms=[:norm1],\n",
    "      kernel=(5,5), pad=(2,2), n_filter=256, n_group=2, neuron=Neurons.ReLU())\n",
    "  PoolingLayer(name=\"pool2\", tops=[:pool2], bottoms=[:conv2],\n",
    "      kernel=(3,3), stride=(2,2), pooling=Pooling.Max())\n",
    "  LRNLayer(name=\"norm2\", tops=[:norm2], bottoms=[:pool2],\n",
    "      kernel=5, scale=0.0001, power=0.75)\n",
    "  ConvolutionLayer(name=\"conv3\", tops=[:conv3], bottoms=[:norm2],\n",
    "      kernel=(3,3), pad=(1,1), n_filter=384, neuron=Neurons.ReLU())\n",
    "  ConvolutionLayer(name=\"conv4\", tops=[:conv4], bottoms=[:conv3],\n",
    "      kernel=(3,3), pad=(1,1), n_filter=384, n_group=2, neuron=Neurons.ReLU())\n",
    "  ConvolutionLayer(name=\"conv5\", tops=[:conv5], bottoms=[:conv4],\n",
    "      kernel=(3,3), pad=(1,1), n_filter=256, n_group=2, neuron=Neurons.ReLU())\n",
    "  PoolingLayer(name=\"pool5\", tops=[:pool5], bottoms=[:conv5],\n",
    "      kernel=(3,3), stride=(2,2), pooling=Pooling.Max())\n",
    "  InnerProductLayer(name=\"fc6\", tops=[:fc6], bottoms=[:pool5],\n",
    "      output_dim=4096, neuron=Neurons.ReLU())\n",
    "  InnerProductLayer(name=\"fc7\", tops=[:fc7], bottoms=[:fc6],\n",
    "      output_dim=4096, neuron=Neurons.ReLU())\n",
    "  InnerProductLayer(name=\"fc8\", tops=[:fc8], bottoms=[:fc7],\n",
    "      output_dim=1000)\n",
    "  SoftmaxLayer(name=\"prob\", tops=[:prob], bottoms=[:fc8])\n",
    "]\n",
    "\n",
    "net = Net(\"imagenet\", backend, layers)\n",
    "println(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "open(\"net.dot\", \"w\") do out net2dot(out, net) end\n",
    "run(pipeline(`dot -Tpng net.dot`, \"net.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "1799×543 Array{RGBA{N0f8},2}:\n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)    …  RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)    …  RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)    …  RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " ⋮                              ⋱                               \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)    …  RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)    …  RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,1.0)       RGBA{N0f8}(1.0,1.0,1.0,1.0)  \n",
       " RGBA{N0f8}(1.0,1.0,1.0,0.667)     RGBA{N0f8}(1.0,1.0,1.0,0.667)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Images\n",
    "using FileIO\n",
    "load(\"net.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved filter from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved filter from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved filter from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved filter from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved filter from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved weight from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:13 | warn | Mocha]: Automatic converting saved weight from Float32 to Float64\n",
      "[2018-06-27T13:59:14 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n",
      "[2018-06-27T13:59:14 | warn | Mocha]: Automatic converting saved weight from Float32 to Float64\n",
      "[2018-06-27T13:59:14 | warn | Mocha]: Automatic converting saved bias from Float32 to Float64\n"
     ]
    }
   ],
   "source": [
    "# load pre-trained network parameters\n",
    "\n",
    "using HDF5\n",
    "h5open(\"model/bvlc_reference_caffenet.hdf5\", \"r\") do h5\n",
    "  load_network(h5, net)\n",
    "end\n",
    "init(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the network parameters, we can now feed images to the network and read the output from the top softmax layer to get predictions. We provided a demo wrapper of the network as a simple image classifier interface in `tools/image-classifier.jl`. We will be using that wrapper in this demo.\n",
    "\n",
    "Before constructing the classifier, we load the class names get a better understanding the numerical predictions. The original network was trained on a 1000-class dataset. The classes used by Caffe's pre-trained model could be found in `synset_words.txt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000-element Array{String,1}:\n",
       " \"tench, Tinca tinca\"                                                                 \n",
       " \"goldfish, Carassius auratus\"                                                        \n",
       " \"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\"\n",
       " \"tiger shark, Galeocerdo cuvieri\"                                                    \n",
       " \"hammerhead, hammerhead shark\"                                                       \n",
       " \"electric ray, crampfish, numbfish, torpedo\"                                         \n",
       " \"stingray\"                                                                           \n",
       " \"cock\"                                                                               \n",
       " \"hen\"                                                                                \n",
       " \"ostrich, Struthio camelus\"                                                          \n",
       " \"brambling, Fringilla montifringilla\"                                                \n",
       " \"goldfinch, Carduelis carduelis\"                                                     \n",
       " \"house finch, linnet, Carpodacus mexicanus\"                                          \n",
       " ⋮                                                                                    \n",
       " \"acorn\"                                                                              \n",
       " \"hip, rose hip, rosehip\"                                                             \n",
       " \"buckeye, horse chestnut, conker\"                                                    \n",
       " \"coral fungus\"                                                                       \n",
       " \"agaric\"                                                                             \n",
       " \"gyromitra\"                                                                          \n",
       " \"stinkhorn, carrion fungus\"                                                          \n",
       " \"earthstar\"                                                                          \n",
       " \"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa\"          \n",
       " \"bolete\"                                                                             \n",
       " \"ear, spike, capitulum\"                                                              \n",
       " \"toilet tissue, toilet paper, bathroom tissue\"                                       "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = open(\"synset_words.txt\") do s map(x -> replace(strip(x), r\"^n[0-9]+ \", \"\"), readlines(s)) end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caffe's CNN on imagenet is trained with BGR channel order, so we specify channel re-ordering from the standard RGB order. The image data from Images.jl is in the range [0,1]. According to Caffe's original tutorial, we also need to re-scale the image data from [0,1] to [0,255], and subtract the mean. But those are already handled in the network definition via data transformers attached to the data layer.\n",
    "\n",
    "The second parameter to the constructor of `ImageClassifier` is the name of the blob in the network that gives the final prediction. Here we are using the output of the softmax layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier constructed\n"
     ]
    }
   ],
   "source": [
    "include(joinpath(Pkg.dir(\"Mocha\"), \"tools/image-classifier.jl\"))\n",
    "classifier = ImageClassifier(net, :prob, channel_order=(3,2,1), classes=classes)\n",
    "println(\"Classifier constructed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AjMi+9wg7LiJpHmm0vXe3IrUUM4NQsO9NyUTurBPolB6WLnQ63NGBIMt+U5KZTHiaSjpHhhiN6sCAVMY0VznNaZyi3748vl6WNV+Uu8Pj+pOXt68eN9+wLXIVEeSgJ2A+aGAYVVJNqAG4SBERICJcVUXUI0RM1QDE3hZARNXdKckUhzMTmSKpJhAkEwzPwQQ1AoxnoiOTycDoDgl3HT0poFogkg52kqkhoCZFBaRHeAd6lpQWoTPkyHKjLp1EJMdo2fXyeL1ciC2u69NlHevDZfR7M4ONISs1xxieHSVF6ZnUwZbTtCQRxyYorfSqdbyNfIoBkiZBZapaZg8MAQUaDtCHRlXL9NGcaZAUiXSMTDgdKYtAlFWWm0kWm02tpDAiwvcLJKlqY4uED8/WfHSip6QSHB4SSVMfnQQTwaAwhqswRs/9r5jUuZSqRjFwRGSm1mp1mnpbr9etbR0clCSVQHePyFBDGhgj+nCfVGwynU3qsDkOS1jRw1KmpZTKaYq65Lbk5T6uFltn1yLY0EZv6pEKahHjNNpAUoo4wt3FJMMjIAVk2QsBSopoRESMiPAc/oxVcecTItIzBvrIRu+mk5NIKJMKQsMhgQxSYRABy2x2sqKcTXpi88fT8ePf/PbfeLt+8j/87r9nff/bH37rj/7971/Wd4NG3HgbNiSk702F1p0gp2oRsQxkJ4slkdgLIvUdP9vBZyAjqBDRGJ5BIRMZSAV3+iMzM5UiYgoHJMbwiO6b+xiSMiJDMtTFKAUcSUsxoHsmUHqZNLNEBKOrSkj17OWoWkqZTC0FI9HdZV0ZPlTq1uL6tPXHx+vjk49MCStXtRFsKU7zxs4qMhGSRauYRKxVRgLJHEwD0JIt0SmippnPuJAzNVMIDQQhCY0IJTJldB9EpSDgXnu4zlIng0K0FNW5lLmamY5sLNl6HyNDsDdLo0EuTcIlBAJHQITMbWzhsjOgzkwIlazsHohQVSNauHkkJJT6wU/d7eoDMVqReSnzVEWt5xgZEcFMIgNBFO6lHAaQO7tmR5sXnSqXg5Vq01LnSZfF7KBzUdkBwNwZqF2YMBIgwyadlhlFzYqqDEYI4GFmgkyOng7A1EiI5IAjEgxITwknenbP7t5ab8N9YGx5kRKiCY6kmkoRy0RGABQYKFBCQi3LecaUFD9N5cPX7786zWv/zObp577yvW++/Nr27gv2EC/u88PbePpsQ7JHM1VREqFalRBCVOWZKEmIghok1Ujd4R0RoQqITHIHd75kF1WEkJ0wEkpwrys1STBGj969b5kjJYTBpNBJEoJEZgSFIsLUHTgJAUWUVEmSkUyOWkudi05mxaZS9l/JzB7+8HR9eHd5ene5PKztab0+rFvrrT0meoaEt8CWMpJOTZe1lqnU2uUp2UglqKwjgiNsXxSRipKZ7u4DSCF0h1P2FaOSCniP5omsGWWku4suZT6UaTabZyqssE6qQhEtqmIoU8kEicRIuKYmkKHpDI/uIx0ZCE/vsQNBCSiNGZG+1/miyiIsWiabjtO0FP3wp15RIBoUqkqdyjTXOmuxQlUn3CMAUjN36AZiFDW4DHQq5qVOU7EZh+VQZpuWUk9zqfV4PE+Ho4f1sQ73ER3IOi3zoZTF6mRaK4U27d0gAMkAIlMS6RnpgGqKuItTggpIiiGAtbdtW7f+dGlr87H5FtmT3SoFsEwrs1JzJ5YpJgrKEFHNkGSRsuhsRZSp8fNf+8Z3zu/f3L7/8PnD9z7+jX/wG/+bn/7wG/pobz9999mnn3388muf/8V95r5WkTlsrhRRhakCAriqi+56HhWzZ8BhqnWqFOBLfG7n+NPBzGcklQmGJLBvEwrAgZ6OvnUfaakmolTufzIZjMBOzyOhVKVaRg+oKERCIggmmeomtU6GipxphaVaBN2ze27Xdn9/ub+/vvv88fHNY1tHDES7UkBVrZqakKCEsEudVOthWupUbSLZVTQCnjnQZaQBg2MMqCs63JFkkknAQylkUhAZCImQno60AFyag/Vox/NS66y1iKYVUilkdw+MnaB1DzDTI9zB2gMBQ6rBGBgte4scPSKHx44iS0AA+HCBqmgRqaoTtVArS6V++N0T6KpSrJRi+/FQpmmeJis21WpmUN1XZlKcDGbGrozw4W6lHA5TWXA4zIej6iJap2NZlsNpOd6IFiZ79621onZzczyeluU016o0LZPtraKWMkbkLoV55gmFIpBgBYsKSM1A7xHd+3W7rOt1Xdvatx4tYlAhkgAz1VggBFV23lEEFE+qFpVQMyNF7FDKocjNqS60l7j53jd/4zd//u+81OPNi/fP7/3Cz/3Mf/Tx7Uf/5n/5J1g357I1TGJAeoROIoQZVQTCzNBCihJTkjQR4TTVealqoACEQP7D2R8DRIoIkoxUwbP6hbsAKD0SHUhIRtkvjwiIeO6EIiOcoRgE6RzJVJKaUkIUVEJEBCohKmbComJmakRSzHu0NS6Xtt6vj19cH948jqdMT4pKpKiI0QxSIhUga52m6fb25vXNcrvUqVRDhodHYB0bImQUNuZANpHIyMgvWSsQqlRqghT14e5kmg9EMNyZ0V08s8zTVIuqlqlGjkCoiRYVZjEdw909YgcolbQYoanpHOE9c4zIgdiGO8PDIJOVFIFSzFjVitZlkmpWRKdSqtVD0Q+/e1TTWut+WYsqREVMIGZTraYmxeo81ToLCzJBdDKUSdIHMrPOPJzmUnRexGaZynyYzqqzSplLMU4ISUip5XCw5bDUqVjVaSpqQiECEZkeva/hnUzIs5Aw0DuG+zYwWl7WXLextr6t26WP1kZEjjb6/sCppJraRDFRNRMVEew7Q5lpiSKWgiIqiTpN50OdS1mW42KnX/76L//Mhz+7+PTZ9//o+ud/yL595ef+0d/5j/53fn+9/PAPdGxpNzJPVurwrSgpkc8QLVgtYYG9eUeddF4mm1V24lqUgIiGh3sguLMjyEQoJVXgiUzZZSJBcWRkQnQkHJpqA5HhGSEgUxhkCIhUT0YpAksxQoWiYtyJZRBEqlrRSVOat0hEd7/m2+t2eXuRh8g1WhdClCFSUlqx0ArRPsRF683p5fuvP7g5n5daD4fTab6b7RjByBiN2cFR+xp+JZuGwzESyEENNYHoLvCTUgolug8P53iWyURyDEXI8F6MdS7lUEJSTaWiqE6zkjI8UxDIgHqm9zF6F2eOaDE8IxzZwxsBqJqKiEgKsoguUy1Sq1m1YmITpqlOy2zV9NU3bq1YKWamZsWsqFkkFCFEpKehaFHSJIrIZFILmExABUh0d4cXoxZhxWy12KlMp2LVVIU1EqJTPR5sKlZtWWar1UqplVowInvzGNHb1lvPvvaI4R7Rt7xe22Xd7rs/XfPpaTw0tEu/XNt13Z5Gtp5tRFgpViqFU1UxVTURmCpFREWoTEvfVVoJNYV175iI9I9uX5XgSPyVr/36t+dvfv5nf/7vfvu3//SPf/sH/+4PfvCH//qHf/4vvv3NX/zeb/7j7371a3/4g//vjx/efvT6g2mqbx4/q6XsR7ZIijGFgy4SELXJ5mWqU9GpKKkkkKY1Ip/l1LuKB54BTU0MMgP7BhjIcKcQ5E79IInEXvU4mUoJRES6p6uzwCalZRZSRRgmUp4p/xSoSqGQUlORyHTNzrW19bHHtcWWHCKs7k0kqCbmYgKRNNc6zfX48vzi5e2L0/HFzc2r+XhelnPRCVQP9G1kbwI0H224D83OdABCFzMTOjBUuRwrD67TXI9U094kPZLYPAJUtVSCqAfTCfNkJjQji4kQlAiMaEmOHh49el57a31kgC0RGSNzIEEpCmMUsogVlkozloOVRUVDZ9FZYdRZ66HozYelKM1ETbUoTUVUCJEMxvD9PhtgRKYAIlAhRcxqZBBEZm+RqVJNSzlM50M9Wpm1FINSipkmpRa1uhfxKpY2QaoEpPXuY43hY/Vt2zbvY2Vr7rb20UZ01z7sSaRJ6Rl9jO5+pQioZEKpNDVagarqrCypAaoZXUDVmuGIjL0szAI6UzsGo3/l5fsfv/f+y377G1/7zZ98/49+9w/+1f36lp7L4fgY+vSjz37vn/03tzO++Vf/i1/45b/2r//p/+mTzz958fqDd0/3qYQWLSqmKpKmIpIqJKdqp/PJpmpKkWcUNHOEs4/uI9JF4MCuGMeOeu0la+7rXCEqiS8lZs+MZMB36ZAwicznO8CEliolLVChQqVlknAV1QItJvt+S3ggBtLRL9Gvo11HbD1TIpGRQqgtjk2EyQFlnU8v716/OL+czy+Ox9vz8e72+GKu88g0LeLRU3o09xThCI9IuNdQ6lAhBCwB4HQ8LHeTHsvpUA/nhSerN0WqjHQnS1Vb1KYynQAbtdY6a4oTmjYkMcZIeRZ2PkMznnAyJFycyAF6cEeBLa1ILWbGUo1FbTabxaqwkFWphKJUmadJ63uaQmjulwZz18+J7/0ZPGPEyHD4iECEj3DdeYRnAYUgwOabJI/zPM3T6XzL3DtW3beKWQ2CKg6nUlWm+VjrNLpv12300Tdf1633rW8yetcprBJgqWU583QyXaiWWvW5LjQDi6iaqGimGlVrTVWKTlZFVSmZgIPhGUgkmSLRe3fYdKxT+GrZf/b1N7739V/Hw/VHf/z72fw0nyP0xfnuZq60ybfyo9/7/9yeb776c3/zZ7/zS//9//xf34/0IrD9BZUyKUsmqWbUtIJlsWUppviSJSBFPBEDrfUYUDEBd/I6dw9SgjtOi9zVAzt9vNP5+5URg0LNxAj3DIglJJDCNDVipCV1P54ckqRiv/CQGbsSJMaIGGxXH9u1PUasysjEoKRAvMHcrGRySAmzPCzL3ctXt68/ePnq9uWru3lZ6lRTgiJIJHJs3h2iZVabOWWPtl2kUFkVVeFCTKdlOhebbFrm5cUiVctc6zwfDsey1HKqZdYyS6mTFWqBmpYyUeG4eozho0Uf3hKpZkiVkPTMEUyJyN040NoAlXs/pbSqYiamWkSK1El3Y9aO1pVaSjVV0emsCGHs8qddpZOeI4EMYIApiAwf4SMy0nuEZMAjRg9CKCG2d2k61VJLXealTgsCCoropEu1SZRUJiJiqIrN1VTbdr1cHrbm2zWu1+u6rr3zMNv5rniMadbjsS5nLItIlTqRSEFRMUCJEpEiFApVyqxWrJRSrGiZTEVFaIQokkoQwTCqCunM0v39u/c47KePX/net37pj3/n3zy9ezidbjSJUq7X1dTKvByON299W//st+5q+fjX/uEvfftnf/+f/18+MQyLpSaKsCDECbqQ6dOsx8NSq4mJqWQGIJDq7uvmo7UMIgUZz4o3IoGUXX8Ih4wYAiZ2KZnvWm0m3QNUZmY6KQnd1UxkKhWkS7AYAcpgyUiKMMJV+aWWy8IjRo6W6KOv8IYvkahEKqAOt5q1VA/XKrc3t3cvXr56/d6Ll3fvvffe+XwmU1QiE8hw760198xcSjnXo+lMSuu9rxkDkl2neryZ6zHKoZalQqNO9Xg+WVEtKAcuN6XuxUmhGKVaaqo4FZBoY3h6IIb34SMyY4SC7ultxPDehrunIBzpqSYpUAMLoaGVNptNpRQRkWdzo5qYqmmZRA+35i3dkZERO8Q0gL3AjAxmIMZwH5mROSIQDo/sY4TvQnqnhohu0YoVFYHIshyqVQZVJ5OiClM6EsIxOgCouuN6fXx3/+Zy2VqPtjUf2zTr7YuF4mXi4SinW60HWBEx6O7/EiMkHEjNIIQUqxVFobqYzVbERIvNtVRRFUgRIZxEtWqmc5WwViww2jc++MZf/eavXX/06bu3n51PL5GhFfNxGm1kmYpATU/H4+Pb5vefvnp9/OrP/72DrP/P3/lvs9hhnlUFmiFIYIiXovMylaK1FoLYV7hopLTW2qW7O8EMCQyPeLYAABTJTA8XFTX18EwAu6HkmU17NthEMIPY9dEifCbRA5BiiRSKGKAuQnkWFHF/1xD04TGCKb55Tzz7lkZk7CQGh3gtyoxA2GTnu7u7u5cvXrx8/eq9081xWg4Qqu72VWtte9yeRgwmDSKcqs21zhLaWozrSuY0T/VY5kXm4yJTKWbTMpe52FSmU7FJKEmBFSkztahYKRVaUwRgUAkmBTt34R7pLonhQYT34R5Jidi9sUwAmlLUiopSTa2aFaUKwYBTFYRQ1EhJPX9l9swIRni4j5G9d++RPVvfhjf3Hrm3XOHjuUmLcPeM7IkBFYpJgTDd3YEULmU6TgeEOlREkj0YIcxki9ZbS4xt2y5PDw9P920bbe0cY5ns5nYuRURwczcdTjofoZNQfGBQYMX2Z9sTbQyhpJCmIrVKMVM1FS1Fq5VSbTKppUhVUkBQS9J6nVgU1XKe81W9++Wvf+/62f22PhWxea42GSMmE4JG2fp2KDafX2UtDz/6reOrm+/81f/qR7/7P/7JT/60vHopDBUOZEogvUw6VatF9nJyp7Uy2Ddva9u2satfsNeypOh+ZUsmIqHVbs7n1++/Wpb5SzX42P1HeyeBTEEkdv1MUkgoQRNFUVWlBlXElOIqKVIEEjEyyNTcoaVkjOzDR2ZRtVJJ5IieIVCODHcqbbK7V+fD6fDivdfvffTB+eaoVUWlTOYRPnZrxHrdtrGtShOrahYeGGGqQnCwpAChBdN50WpmuhxnzKaTzMfJqmotEKUVqaaTWWFVTtVSxIG0rVQ1pXA/I3ZcgERGehLQUqZlKlV2d05RUREDlValTmbFREgmqWpKgkoRyd1UidAPf+q8G1zGCB+MnjlkbGPbfF1H66MNH+6tj9EzXDLEffTWwzPCKaKlgEEdQozRWXTtG9xvjqfdizh8be0y0kWkeRu9XbbLaNfe2vXpab1uo0c0V+bd7flwnFXydF6mI5dF6iw0eKwQqpqqxg4RO3O3jjFCFVJMtdZ9bewm/CIioqKUomlipZqWKFWl5HmapmJztV/8+KfP/bg9PRxkynSdDjIyw2spggBZaum9hW8vb16D+NGf/Nuv//xf++lf+Mv/9H/8r+9LK4eSCTp6djOdzeapVDPkXr8zg6OjNd+uYzTP5PPFxdzrs/1cz4SYnG/PL+5uXr28e/nqxc3NqVrJzG3bufNdbL1/qqCM3VSRYLKqRcnMoRa72VAVQArKMwNNSuqOBWTkGEHhaZlOdVYzLcWUEZEj4UzKYF/O5e7F6Xx3fvn61YtXL2s1raZFVLW19el6fXy4v1wfvHXJoVpZCoVFpah4OFpEyuhDYhMAZmWqx8PCyepSp2Wep0kEdZ5KLTCRXdxuIgIag5KaYkGE2TPkvJeNe7EekhCrdaplYbIUOxyX5TDf3J6Pp+PhMNVazLQULSq7l3339DJTIJKMCGTqR9+5LSZmJOjuPoJCqmzbel19XeP6NNo2RovRY/Rc2zb6yIAPzxA+Y0aRGCSpu6glHq4Py+lwez57v/a+9tGHuwjG6Nu2btfH67q6R1t9vbZsnRjzoRxPpda6LFLnrDNqYZmKwyNXcLeiyp5ZES4Ky0wBXQB2KlQrYaJipSj5pZ8zTWAmZQoVn6rOUykl5wk3h/NPvfruy+309OnnBbR5TlOL7GOb52UgKZpaR/d0bLq+PrwXl75e//xrP/OPRNZ/9i/+CY6TSI3eUqSYnus81bqTdiIlg5lw5+Xa1kt3B7AbOYO5SxWxqyisluUwv3x5ez4fa7V5mm6Oy835sByXSF/Xdbe8ZgSCJGNPLcgU4f6apPheUD3LLvZzMiXCny+ifLa5JGBqda43x+Xu7nxzvjmcTtOhTmYR/tg6NY53xY55fnl4/4P3X798bzkcrIiYUC18tNafHh8fHt5drvdjXCAwNQjCk6FznapNJaT18L4pny2h03GxpZZSl9OxmGammGlVLVInNZPdUJOmVMhOgANiSkpipziRSCCrmdpSYJIqkSJSqpViy7yUarWWw+GwLPPhsByPx3mazZSC0Z+ptPQwK6bWY+hXfvaFWFKH07XY4TwfznU+lDJZpvvwtra2eW/hHT5ibas3yZGtXxlUeXbxiRJQJpkRY6weMtdXL++YzWOk7CBWAONyeXq8PLTWex+9e/T00VU4H5YyazWpJcocVrVWUiSiAyMwSIXIGL31yN2cFpG5G3/361EoYhShIPIZOBeYSCk5zVEspEAZL16ellJf149+9vWv+o9XG6Ntaz3MYohIAadq0/nm4elRokNytulyebp/Grfvf3D9/I9Pdf7eb/6nv/1v/m9//JMf63zT/Mm0FJVDUVOSUGoCnsjguvm2+s6N7s4KfTZGAJkhIdWOp+OLu5vTca6TTlWXYrPJXnIk83K99q1lYncFEDsXttvfQEWIAiMpCaOAiuQOWCc1zWxf+TtyY6VMtSzH5eXptBzP5/PNy5d3ty+ON4eTliXLmGY7vSjLy+OLV3df+ejjly9f1aVaJSVVZfR4vH989/bdu/v7h3dfPI03SCpLZuz9fbSBAdKMU2X2toX73mRw4jQXMYqqB1w1iGoqQlSYGoUgAnuH2dMiPSm5f11URYuxzDZXmYuoUouWqZZa7DmVg9xLHqGUWkupVmyepvlQaq2ZzP09SCQTRv3qX3oV8B5eSrm9PZ/Ph2WZl+VwvLm9Oc+H4yxa+2i+DbbM1hHIETESu/Nr1yiLBpIM0nenT50rNW5ulsOyhJCEGike4U+PT5eHh8vo4TEaxoiU0ILTfJjMdEKZ0opShlTQyurbCBcTgBnaroHYNSTJIpo2RqoLArtqSaEJFahRikB0mEGF1VSLqKIyU/yju1ffe/m3yv3U7z81nc00UxnIPmjytG3zcu5rtO16lGD64fyynqa3D5+8fvl+vPvx6es/Pwn/2b/8H8ZxyW2QcrBpmikJkb3wsA6sMXqT7mtjN6HCqJLiZGaQUoGe2pdjffXi7mZaJqLWuiclHQ7LVEvvfb1u1231HoApNQO7TMyJvUMoCjfZtWJWCg1SQyvVUkt7vmY0MlmyTDrP0+nV7evjq5u704u7w93hMJ+Pp+N8ezxON3ezTQGJV6/vXr33wYv33j+eZxRXEcosMdbL5f7h+vbt/Zs3n3zx+Pm6bs/uOFI9GNpHjJG+IlobIx6urXdHSx+ZViloaBTLlNCuFVCFRSKDzT0ItPUKjl3/ztjjNnZNCxVaRQXp+xaXqehcSjUz1aIoWqpSrJoV2zV54SOQhWZFS2Gp6onu4RkZXb/xSy+BnKbpeDouh8M0T8thPhzK6XQ639ydjqdlqVY40nvfRjSFpQsyn2FrEVUCQ5GADFJMlmVajrMWlDnfe3kWI8RT3dGb98t1fbxcvA9kpjNcMoeVrItRIJNQwN01ZqX5uLZLz0iBB0cn0giNSAkxTCQQgkB4rKMxkhAVI5+hRRFVnZQTZIBXFpwPJ0Ver9dvf/Qz9XPm/TvRStE9sEoFKhjeW+un44vWh0gZwYxxdz6I5Nu3D8s03928+OrPf+9P/vB3fv/f//5tPSyTScGkoTJRSjqAAkr30duGhIDJvfTZoetUU0hkcESK8u50Pp3POpVadbfriXCaS6lla9u6rb1tYGRGEEmFKJ5hVEkUCsRIDkjYZLCkDtCVpFZCMkDRorqUMp+ONy9vb44vb29vz69v7u5eLGVCiUXNFp2Wejgu8/n4+sXdYVlYdTJgaLhfL08P794+vbl/9+aLp8u73i/behltEDQxJkbfImP4yMxx6Y+X6/W66lC6XbrHiIGsdValkGWazEwVQiENu2ud/pyIBEbs2gdEcsRAOslMAYs+W++KUBNIlQBUd2aIuxPjOSQhdw1JeoSq1lqoOaIN7wNDv/urr9V0nubDaVmOyzQvh8N8OB6WeZrnpdRaSylVbRJRQWYORCRUShHqjidBVYTwtJExz3a+WY43i82iZcyL6iRSAjrG6MPHGON63cYIPGNzifRkirFMJhaZz+6QyHhaL1u7JrQnwmX0TFomBKpZYmTvTkg6IHB3HwNQQHKPl6CqqqmSIzEiRk9MOkmOysOL9o35E4m49vC9kTIrNB05hBzXcXPzcj4fbZqt1N7Xy+XdNB3c45NPPql5//I7v/7Njz76b/7p/5kV58NLRNh0oOxqOIogKKMPeCODAupelhHQ2MNnAE0dkDFGKXlzd3c8notiOVixWqpGjDY2UZZqIxrS62xlohRogSpMAQTEyUL4njEXQEqoJST2g3OPhCEpQK31sByW4/n17evXdy8OL47TfDgfZlXSjEVZy3Qzv/fei2WqYnKYalXp6b23d59/+vDm4d39/f3Tm8vT/eX6eO1P27qt29p7AIjoHukjYozW0K+xXp7G6szFB7btmllE7XAzi/I5QUZTtIwxMgJ7rFlk28bw4eFbu3pEBt09kEQKjWJCPAM7GSKkWQIp2DmWzBB5NtDuEoAvVVsiGlRYMQgDqT/9Vz5Q1VJLrdM8L8s8Hw6HZVmW+TjNVuuyS2vUzHQCNBG5E407HSvALrgSiZAE6szzcVrOy/FmmRYD04rtp9G2bdvafHhvY10jUoZH7y2wP1zuR0F4D7SRrfu4rmtvjtAIQSigHhkhiqKiQI4e6eKOWicRGX1ESgbgQVJECQdbsqeHcBniDNgqH9Sv/tLNr+X9u4tvh1KlsG8B7Pt8cA+lU+3uj+++OJ8WLbq24R1COZ4PD+8+ff/29vV3/9qP/+j3/vDPf+/48raUWdhFTCxLISUCaOHD29hpKw+mMjWZgZ24KQJJSY/W+zicT++9fLlUtWK1Fmh4ehvtuq1OVzOasFiZdFrMZtECK0ILqVCzgbHv/KRLCZvEStnF/3tSJQJCMZvnejzN55cfvD69d3N+cSoltRhrAUVlrOj1ML04HGr4a61H58P6VFq2x8u6Xq+Xa4zx8PDm7cPnPbZtu7S2efjIhEgkwqX30a5jtD6a+5p9HdEjPCN1G8Oj12Wu88TcQF/dqaK6n3KIUI9oo/UYHo7Mnf8xEWWRLNxR0J14IIUQM1B9RAwPIDJGOAXULzOfcrcuQSVFEQjRUkvNhIkpIoQw0WrleDwcDgezHc6S4ay1aqVK5XhMj5Suq4wxMrm7XYDYwY2AB9ObqMyHw/F4c6Cmlh7w4Z7DW2vblt4khwvTA733rV0BzKyqOXqPnolNy8iM4egtYve/JCV3Sb1WKxIS3b0HvXh3U81gBoUVyaQ4YutObpGRwioGSkQUvFzX+PbrDz6Yv9Hvn5yrmbXrKAertbqHd49IE57vztftuhzPWsqPfvLjWu329hVD1utbpc317gf/y//rp97/5n/5n/5v//Uf/t9FU2zZDdohTAaSlAFuUGih9yxq7nR4ZtIjXQm6gMwC27b+ky8+ff+9u9PLF86+9kcIhg9I2Ey0IXMeZKopBJjhOVpfw1GGIs09tNXeMiJomdnHgFbzsZvglNA98Gz0DrJMotXrsS6laLGGyJV9rE/r9mpZzqO8HIcP63t3Xtfr1ubb2HBd158M/NH18X69//ztp1tuyOE9HR4ycjxp19Sz+BYeQ54tr11cTL15T6LL8Li+W7/40dubm1On9+yYJtWWRUSTktBIuhYtUSKioBAJD6URu0NwTzLxzEB08WS2zBIB9z7ASY2IMpVpmpSgRtHcL9xAZPquaCsq52WyFESmSqilFUyznm6Otc5CFegY47pJZI4t2mHrvrQ48jl0Yf9XnvHo7KOvAwDvzOaCWlnqUkSzlqT41i5t9NZibLqNkb2Hs2/Ne0DYxiaqCunZKGNEy0TvsvUI5YiuWTMTIUimpkRGR28cw000k21r+5EPkfRBncU0RJIWEI8ipYA6y7ke9bM3169964bXGqOVrGKGcFpOxSDsPVq7+pZmh2VZ5uO5tVX6Oq7XUqbDcnp4/KK3prP9+H/+v37rH/7jv/5X/+7/+/d+q37kwJwRiVROyIBfBWOH88CgYQzfXTGSYhQHPbukKgUSn99/9sc/nK1wnjzTEx6MpNRZD6jconURqSaK9BHdYwYkHa31be3D5755RDS/tGx7xgR1IAOwnT4Y0bu3RFSbEIw+yqJU8dxkjLssGaefw4uv6ElEquNoS0wJQc/xpJfbng/x7k/efl/AI+b7/oQARHrvIzdIYlLp6Z4jUtN6RkfbcgxXCY3wTLSH/sa/OJ0Otx+fenYbyfC6zCKEDmCI9KlKBkcTUYSPzEw4RVUKoPHMqaq795aZwzNGeAz3jKfhCpVi01QOcykW1dRT9mw1AKVMWjCGiw4jhQwAkCyT1LlMU5nnyeoMxuglHnXrXqZS5jq1aZ7nMVxVwedoNIokfNvcpJZqpUzhSlTJWuSghSrPJWE63HNbR197b210jus2fGjVCHQPwCIdMTI90va1lC7DN6REwHvm6Gu0ojWT/eoqIkVGOJV7kLJUWNFSxYwiNJ2qzaXWYseCCau8+PDFfJp8PSADKX0d82yO7N7IYmmTzM6xbiPbxQf0eLteHmZN7+v2+NaU77945apbe/PmzY8+fPf09379P/vXf/AvbPEMiSHInS1ft+hOoUYGlOjeAxKifexRY+q9i8BqFOVUliHj3dMXP35X3ru9UdXIvnsZrNrBDmJivSF1qqY6Z6bKtOe09t63rY+e29Z73y5rbmFpw/NKSWJ3kIVkiACm7t56Msrl8fIwV1DF5b2nw4tP++11soGe/d3D26enJ11mJiTixc3t6w8+WI7zJ+Ppd0JnLtfW71MiEHtUUvCyrcBbdLYWPhBP0tccgRbsvUlLgSa6wrbH8ZM/+7yeZpnpHEPFcRURNVDCG9z3TLQIMp/JckYMoVFTxXJ0LZR6QEX3bMOzhWrhGG14pl8u68Olz7Odjsv5cKAZJJ8zldP31Ewz05/61feU4oAVO92c5+VotZaq02RahFbTo/etj2gtxuit9QhX0x1fmeaJphALtcnm43w4zKdSl2U5SZ2VBjNTTdfrZW3tabTwNcbaLm29XsMHItuzw3UP2dqrg4jeLYMqSi8M8SYIRnO/jr4OgTGZMYSGzGBAGSP2VN9adS6ziIqWYjYv07wstUxTKRPYonx08/pV16ldL9dEHxERaEGaTMAeYbZ3RibUZZlNjSb1OFsV76uk3Ly8DUpuepf+0be++Sef/O6nT++m+XTJ5myZrfnTSA7XjJ49vUfr6A3bhhEY6Rlu1Fo4HavMWY3LvFAR2EwNqp57jCPN1EwpMs0TJ4IUNS31cFhqLVp0OR/uTudlOamKGups8+GQTAJmu2lTjUK6A6SZzXeHaTovZZlTVO7bBz/09/9i8O16efPu88fHxzdvnx4fWSzX1h4vD9end2+/YPLr3/qmmXz25ifvtodH9nu/j3SoYodxNJPuznUE+2hNLg2+JZ8Sm8bgnpOaoKekZDngeK6qbMzgSHoM9E33mLDWmvsQqgoFFCpRRKRY2fPzpmkq1WqdarVSRWUXjKeQZtSJWlKUiaDqfFyW5XCYFiN9jBi5K5n1u7/6nicgmJbleDjOxiKpZjZL2bOxW2+t9+6tb21rrW3ukQkrWsueSV20yJ4WtxyOh2VZ5lprMdU9ODCH+5brtV0vD30d2axtY9tGu67hnWp7oOb+tEUSuafpwx2tZQSzczj66mNDjIxAjDC1COm9DY90hfewSM2pFIpp0VKLCq2YlbpMp9mOxU5lmpq3RcrXDnf2dss9u2eMHjEiA4PpyZHaVbWWQ0os83w6nfa4M4FWUYb31myq6/Wpj8fXP/Nzr46v//Vv/fNWo42xsecW28gY7CN7S67uzUeLvko0iDMjKbAqdRIx0VJQCfFqZbRO8T11fRcnRAYppVhZqtmkZTeaKBVatE6zFrM6TVOlsMyTFJ0sJxUHErGYgZkZ0D3AWQ9qy3IqdzeHrLdv8eGn8vGPIt88vO3vENBIWNV5rlI94JmzTTnix28+jWt+68OffuuP33/77z/v10t6ZiGRORwOpYgWU8IDgaHGGt37UxtXpkv0TJhjpNCmLEeZj5NUD0FHMHbZcRMR99y2FlGUE4MIyx0br1OZy2Gel8N0OB7nZbZiVnVe5mmelmOtlRAPcZTQSUpVqwWCKjiep3qYAqJqNIF7ETMPSYUVrWa6txj7uoqK0cNlD+ZNdEFEdjBqkc7dGylihBhVJ8d1XIlcloOJYah3wjU36xmtbddrW5/QVh+bb2tr2/gygNoJCSeBsaeE7+mJAe+IkXu+3e7kCEcM+nOqznD3yIEUoS/HIsiIEIGSPmKIW5VMhAs5TfVcdR5FjvNpBB7vn+LxKYdnpiZ7M9GhYkyjFh8jJYXuyWvb5vOtbMj+1NuGMdJbKcLWzsfT0+Xd47/9/k/9yt/86W/9d//s+/98uj1j1ZGeRO/RriObj5Ztw7bCR+QoEUjlbjZ12TXjodpIRpDUh/vL1kattszWB0oT4rbWk9G0irBEzxHiEUloEaX07t3Dpjod5iPm9fL0dHkcKts1E2sO7zmmDkmfVIodWGq+2eZPLt/s5YNSHuKyWd7y3AX1eOwDvrYqmpPUeeHWUHnA+OzzT756/+02+ufvfjKq0Yr3Jsw9AEY8A+sonoWMTEvvPWyw0sl0CHKvxgQx1aL0dl21TGCCMeCmCmZ8KTOOkbt2gRkAqFTlPM/TNJlOtdaIISWAELGTWAT6WNbr6e39u6f12kZjxlLLNM2I7N4hrIeSk/m1B6X3boMdQBVTJtJBTa0Ro69baIRza2vr1zG25muPLXdXcz7rdPdoKDBrnXofrXWQ0zyrKlPa2gWklsv6+PDwdL26b33btnUdEbHTmDHIZDCMSM0WCWFEE2iCkTqaiyMS3jMd4fSBwPDnuMUEfA/S1sgI79tQrXC04U6hDfj2uD1aPdR5mW1S1bqlv7mUWi/tum2tzlPH1bdtqjrXShUtE8kRMXp/9/bNm8++OBwO1+tlmqbtej0uVeqkUuqyHOcXP/rB97/79e/8g7/8D77/x//2gvqUbL33HtvqucFXbb17l9HH6B49IapWkxzuqkAO6RTugWMp4pnDr5EhhTNDWG20S7QpUMkIyYRtawdSRYcGyw52DitSiowoucxglskuhZerjr617AkzzPN80mq59Q/H+JVyfB3TZV0f+7aLqIy4rqvqdDgcYvhCkYwW41TncpWH9nTpl1lKraUi89pHDGOaSHSmRaq0Pgya4Sn0IEKp1QqjgR0RHaDNosX2AOdwl+GwPZPZVAvArTc8UxoAJBlF1WqttewRPtOiZKSnwEwppqo6AgU6nayceW6H9dp8jKlUQtPdI5herUKSnk4ZIfrtX3uPshe8dphqnSeqJkZGkhy9P10en65Pl/Xy+HR/f3kc1z7Grrl6DmtTU+5pA8IxNqnleHPDBAQjG1Iy5bpd7x8erpfL6H2MMRx9Gxkg5Fmq7kTCu6e7JOGIEWAJh7fAyOgxmvuXfuEvVTHMHKVwWkrC0SODka6qogwQVAUpMXwIdS6HSjVydnnxzi0wvD2tK1BskTFG31Zy7PJy94SVm9PpeDyObSvTNDxKVZU+um+jb9fhnuj99OJU4+mDb37r+u76O3/6B2+zdY/LU98290sbIyIyR4wYeJYxA4qgY++F98QzGmWiyHDPCB8NAya1aq2lCKBiioKQGL5eW1u7I0gYhEGIACLCatKfbVTdiGmZtB7T/dqeNORufhXn4+z5G/7iN1//9Hs8P2m83Z4s8jAdERySRaxIrbVCQsMRXmYT+Ag89se7l696xu/86N+tiKfuHgOEUxkmwRSEKtOQwwd9s1iZW2ZzdEdnBoPjeFOOLxY7SFlQipYiUoqZiYgwkGy9jz6MVQBVsyJWqpmJClRtmtRAzTLZPO3UL3d+rMyVRgpLscNhOZ4Opeh+y1YzFVTVqVRRHZnr6LbnTgXoI0f0bWyxcS5IS/jova/b07pdruvlsj1u7amtYaJic8TuUYhMyc7YxwTVvKwP1+vtVOa4XlnS+6i17tLF0VrGSEd6ZNLHEN0LnuepSEGao0dANAaw9V28PnogGCMjdmVHUij7PDPKUorIrnpPAKHZ1o1K712a66iFc4avuD7mQzknStGcJ5QYfU8kfnp4lNVfv/diqfrZj36yruv5fFts8bZdUW5fvjy/fr9t4zjG9enN8eWLd2/eXq5v2/Xy8nwevaXjcn1Yrtd/+Hf/q3/5B//y+vD20z62FqPpZFP4xUdDOCmq+wgeFzCDCuPICGpl3xrSPOgNTA8fazTpOpcqroBGw8gVMgV8vW7ryO1+PZ1mn7yollOhTB5iJDPSg0FAILyltdPNgnYOzkuVzl+v3/zbX/mFLflArO5TrSVLyzS1xarTqpb0UBgUqgbJEVkslqmg8f3bj14st3++ferKytJ8JFJM9qw0klBIWgJ7cjUtsOcbjkSaVhVFqVgOpiWTMcgK+XIe0X5Yu/swmUh9DtcjxxhUQfMxRjdP6qTVatmD93Y4yqUTOk/nqC2iJ3yM6AH0iTGidQHNSrEayEu76Le/93qXy1ByT57arXoBH94v63Vd19ba9Xpdr9fetj7GjvvHl5NjdoIJSB+ezj4cTDX1HOHRxrVn63G9XB8vl+ueC+LRnz01+Sx5wpc5Tw7pfVeeIvreBmBsw0c+R4ZE7NtpD5YqtofHUSmgk6EGUEj23sM7kYA4drnQeGpXy+k7p49OF2/9elkbRUdsvrWnp3d3L26/8Y2f+uyzT6+Pb4tyXk6Rcr1e23Cbpo++9pUynyBTmWZJL6bLpMv59ukyTgfrD4+nj791svm3f/f3//Szz8Vxe7oFCofvNrbnMWAAmaZiNKUEd6ebIKR1b5dtXFffvF1xvTx5tOPpsCwHpcmuVere1nZdx8P9+uknn10v6xh9a9u6Xnv3CBc6FX3bMuFM9+jwcD/LPJeDUf7G6Zt//+u/MqK8fXoHH7IO4wRaEat13lNkdqOZSGoxEaqKioUz3bVMH3z8td/67Pd+782fpUCVRMo+r0qShY6AO1wimKOgp18xLjIuLt0IyJTTuS6vzA4UFS1qU9Hdig4ldQwffQBgElARUd1FnhADJWVPt9xfz31/mKipyJ7ECipYIqSLQEpRMwHERMukpQIBuoqEp379l9+HsBRV+zKQhsPD9wEtbdtGH917a230PZqo58g9mC/pe3jol4F/GMMzw7NBPOB9tOvYWr+u29NlW1tr29Zaaz48Yp9Kx4y9PeJue117jA7fMjvGwOiIkRxoY3wpJ/4P/AO4Dz7QEJGUSLpMqQbuurA+fIw+Yhs9E731bbs8bNsypp9/8Y0lvV+fto4MVmJ4UuSzn3y2LMt3f+Y77+4///zNF5sP9x6+FrXj6W5rI8LXx8d0V6FrWe/vmRJzaZd7zetBbz76xV/+7/6n//ZPfvxn3/3wWzHG/eXdRJnKpJbYre/6vOMTIc9pXrmuqw/0tXm7xhgxQohkd7Q6Lzenu8kKKICN1kbvCH18WH/4o598+tmnYwxv/vT4uF4v4etwT0ff3L23GC2GeVYxr/Vm018/fOPvfufXyjru799pJDzLfICImJT5oCihOVkVEa0W0arIVC1ikABsXZ8e18t773/0L3/8b//wsz9dykxAiyUDMpBOqJgMDg0A8M7syUY0ZYd282hy5Om9U70VnXFYDlMtyUyoCnf8o/XuI6tWshIQhVGEWaZipZgmEIi6j0sDe6laylRKFQFZzRQ6oCHFdvO+iKnsokDNCCIihw9n0iKGUrkr06OOEREbLCapBMYY4RkRYjYdliCQ2tDhEh7urnJNmKQ5BRwpm5Ce+XS5SBGmxz4DLnobW28tRgv3dFCUKbsAMMPHIKGeBbnuhJcAmYz/sNypQGY6mCQSoALM7g1QZi9UVE16hEP6Hhrqg1qU25YrJhusU2ocD3yvzINtQEe2aEOkki09b4+HP/uTP+rb0ze+8XPhP8jK081NKXUE/v0P/nBbx4cfvO/9CZGHOiFiOr98eLp//3YaI64rxuOPZn71f/23/pM3D48ceX28FC0OWPEpZyc4wkeoMJLD03OLLr27eyyTEelgmY1MkTbxsHnbtm3brlVmcR1oiBjhKhVAtG2s68NP3uRTpzHl8+kw3718eXd3N6uNjFWEsVUpUB6a/saLb/zt9352e9vfPl0BOnUpM0xFDM5IaMFsi8YeyJ4Yu5SOAg2mYQXg61DgWOdSlsN0u2brfZuENNl8OJmAKkURG6WAh8Grb6ZZ9kkQstzM5aCkT6XUopkOVsDdIVbcI1xUCvexvMgiomqZiVRJK4URiD42j82fbALJWs6KStVqTKaLpVrAo7eOLUYmzVtj5Bgj+kgP733zbnURKwwERUd2HaGqHH7dRRIee/zTVLQoBUNDERzbIGVE9IZibDr28Vsi4shnQVuMiD52tVxy+PCIDDAF+6C7LBJ7Vs1zeokgLRGOEZKelkHf7zSEUbKLSSSIfczCEFS3gXBBTSvJgRxUlfTu2xgSrjGaUbbNsyK6po2Pbl7fzIc/v/w4nJV1o48Y+wSTUJ5uzp99cb/1H3748Tc+/eJNd0GSVV69eAnI9bIep9Pj0xc+13TcPz0y+9u37169eEHmdv84f/rJ3//7//mnP/jB//F/+ifL3bK4vWmPk1ZnFEjAQVVh21yTvfe+2da3Ses8VyuZ0acTr49vtaZprd1GPvaxunvPnh6I9IwuLRlVa8oavb17+9bEUuPx0nqLyvKkYqLOZIzN+QsffvUv3X3wU+f3ntrj28vDCFY9TFUgJTNjeLFZUTTJAe4JpNmpkone3bSAvrY2zyUoo+fN8eb2dFumGZtItYFHYqhkiFAp9BhdsirROiCTSHR6Z5tPtZ4Lp46JEIlkmmnVQHamCvq1i4tqARC90yQTIlJsqpOZaY9gYPSHy2hgjTY/SVS56tnKNBcyFbUIlRHRxRj0GK4xio1122fOhm8JqtHuXtyIoPceEUbLkO4JQLypCBBmVZXhuceRZ2zdu6a4pwQjdn0DsBv+kCAIpmTQI7yNAFIgnp6aSGVkpOR+vBNIiBOBPTzHQwDJ3iIQtAxkuqrCPaERLlTs3rPsiauGeGZXt+yMJOEcQyieohtSpU/ZswN0H+u1zFIl+/Z4fbqPQNITQ0wlY4yRKarTq1fn62W7f3y4uT1cNp+Ox9PhCGBd1+X1+ebmRt8dPvnxD95/+UqBy2W8e/uwWJ1vb6+R/Mmf3nz9F/7ab/yv/vvf+n/Y4e5PHh6X87GoI0JTbmUJ8P7aIkdAmCIxXs8HLDO53ZzvbHLwnS6WKKqQi6cbAptvYxv0jDT3oZq+rRGrP+cdWY+INfvaYnuYcUjq7evbN+PTeYu//PVf/dtf++U75LvHh4frdQoK0wqEBhJipVSh7RPiVEEmYhREQqjZxwhkqWW5fYGHt8U92vpwXTNEwWVJjIGwCMkhRGekpo7Y53vQQ7LnLrO1ReeXpd6U2WSmAtayiwLiBvMxRhsRkGQVNZgXY6FSTOV4WqTW7mMSDl8pZ8rzNGwffLpf67zYHAMmIGW/uhgypMgYEJohmFXVYwcgsZYQ/Sv/8DuHwzLP0/MI1OdRkARSVXevjcgOX2RwV86ZQtMzE6IagEfssym/HGKekeHZdjeDqYookJI0iiSYuWdF7CZncSKFSg/PEEVm7EOYclc+MlxAPk9EJRAig6QWQtQtoaDASlL7wEZxJigUikpJNyYCcd22dl3/9rd/9Zt3L3/82SetCdwzO2nM9EyTXQfBaTki5cMPP3716vW6tbG2N+/eDN/adn339PCVDz969/azh/v7V69e1lIz/LpeDofFrPbt6XB3e/P6/cOf/cX99eELy/fqsRZV4LbOHxzPzcdl86VOkYNQeNwcl48+fn/jox3He6/PVvq81MN52nBN6YUhtBQZY20N2M2C3q/X6+PlYXjfpydYredybL15axpZR4i3N5//+O9985f/8S/+xntuT4+XLbpkSqDWiaBKTSrV5uWYkEi3oga21iQD+/x3CEXrDpM4H9uDVq13r/7Jj/7V5/V6nI57ajhVPMXDEyP3LH2V4cObY5RsyA3RYypy+/o032pdpEzCSTlRqwipZZ/JHCparRi00OY6nQ51Wc7n+cZUZM+YdlemqKpoRqimcJ8dKmTsEYuZTA9RihHc1xVExMO5pwQ4mHTv+it/9xt1ssNhmWshmHAKwDSxskOzqnxOLUbsk+XCdxdLZAxE5j7FBB4+3Ie77z/GBHO3+e/BtfvSlwylGHaii+5APKciM1NSwnfeKzMCGYjMcJlMipFJCdFhVUQsKFlhlaUWSphtWobAlFATpCakBzzDgIzs6bNOf+3rv/iNVx+9va73b5/SKYLehqoN78ycpxIZiVS1zz773CP6aO8e3lHk8zefvfvik4e3X1zv73/6Z3/uj7//h/cPb5elUnC5PLr7Mh9u5vP2dH/8znfnbfsXf/jbr25evVY96Pz6+PLrx9uTTQ9b8xbffP2B9/7uzbvXL+/WcXl1e3z/9fHy9ONvfeX929vbKlImb/kIwT7TnsqxZV9zH5QA5DbG4+XKSKNkRkJfH28vT2sMf317O7fr4yef/INf+uv/+1//R2eJFtv1usXWp2myWgkl1VNSpZQCKBO2tx4eYrbnFYkoyIwoqipIysPl7Twvh48/+lfvvl9v6oS5Ubm/4ojW+j6u1wWZQxLpqqnS4U2YsRztdDrYMe0AWWiTlKpSVHUa7gExMaPOpU61HJfl9ng8TNNUD6YzEkKoyg6/BCIzw2NvDpEFaXv2R51KpLt3CimiJqqqUI/UJPaAaZdo3vum3/t731FRlX1QBU3VHaPHs8NctJaqKvu4bAgjMp2R6OGOCGSGp/tID0/Hnj2zx56F7iOJ9zAPzX10IhB78uU+VNN27IsZIzI1HeGZKZmSkT4cyFKnnIIySKFJrRDVLIR5mSk6RPd92hmQ3BXRzkzEHE7JyE1jGDk+fvH+X//Fv3zmPLZB4drWbM3T95gqgvN0PL94eTic67SUearTdPfqJU1v7l7cvXjpI33rb9+868j3Xr34/h/8XsAzXBnH48mFJjLDl699Azbe/dkfzTnfzvVuOv7U+x9+PB3v7y9p0+s6v386yuD6uH7t44+efG24fP3992t1sXj/5sXprl7jPntDEpARZdtAF17HIwg0svfW19asFAI+EmMsy83lsjHzxXHR6/UXv/nt/8N//F+eBr5Y7z9/esju0zSv3WUPELWZpdzc3Agx2phLESI9ixqLOFwAFWGKmkxTFbWn7WG0dijHvF3+Au96QrVk0eesHUpENvee0dE1hW4+SnTkqhhSTA/zggJdUJbCQlVRzbBwSlu7JiYtM+1Q67FMixYVFp1sn7jKjMjmYzB79tGbe8+MbbuSLopJZZ5UuYgSDE9XUxAUmlkGYwQBH8OH5wiM6GOz3Sf2PH2RWspUWriBGQpV6JcDPQHAqIVTZyYHzOhOTwhdIr3v4qA99Dsz0+GJoBtBTTUIkiJJg4sXYkCRRRXDA4wBhOY+3GAHpnxERJmMk0Ih2tWECjGmksWrpUgKlIwUOss+ZiWGR0BlkqBF5pCRQqpJMZNajntOt0CV5kzfgeMyn4+nw+kkZQ6y93E4npPw5tM0Pby7v7u7e/3+R9E6JH/4gz/7lV/+hY+/8rUf/OBPXtydpmLL4XQ4nbbsT4+X23c/vL157zc++vrvfvFFwObz8auHUyHc+/TwcPvBR5+9fWOv3ruzedF683pZy8Oh4/zq6938oxdfe7d9cns+j1UZ7wr7ooeWy9vHN9XZGUC7jOaRp0lEy8N15chIrJfrbGyxnQX/yd/6B7/+K39p2rbP7t9d2zZ8iJUthlIAuACm8zy33lV4mGtRAQThwdjDEkWfR1NPU21jTU/1rGXR+VhrTZvG+XwYPp7S2wYMI027aqtwSXDE6PAeoyGHgyhTUau+DyCAMmPs4248t2w7KmgotZiCe28+Momh0aXajpsgc4zmvo6BfRpka6t7ETEvbVtnQU9DPTLBMbxYJbKq1dOsau1yjeHu4AjaPtLsy+FU3Ie77z1AhlmtWopNkb53WmMMRtJdM4AcMSJ8t/C4jzF27NogyOGZ+0RcBuPLSaHYp3kWJURddsiy+95YZJKZXyrXczd/QUspUhDSi5kW1RKpPoxlQmgG9pCoVMkQ0iQS6UOSMebRNFsYS/Mc6NMsp8OLDz/4hjXtpUeB6cS4F0F01Kpai5jWeZpPh9bazc2Nj3y6PDze39dqb9588dknP/7ogw8/+OC9v/jh0/Vx+8Gf/em3vvszb+/fHU6HeSrdY314vJkOodqetumDD977ytceLk+uy3JzuOspZt95/eq8HLatz+eXY9J1Oeo0v96688Xrm5u38YRqXy0f3g0ziVjevKi3X3z62VwOUm5+MDrn4OOldy+lTpOZpOmMFg/FzZabMs+Tffzqg7/503/pH/7a32jrw2V7t/U1E1Mpzz0emJZLPWmtERHDpVYK3F1EShHvPSImk76N5n5cDtu2eXREqprmADCdz/Z4WsTrU6+bmVYoMrZ90hmC6LIPxH3+iqQYacpKM4tkH6NMTEWLUEH07tGq7qEaiUxP323dkRtR0l2YYs7eFB1kmrfe6KGGbXvyHGvb5P7+fLxbxnL0o2rqtAvK1GnHw6xqw4pxQj6Nsa1br+Vgz+xzAFRVcwSYonxGi3bZtCITVavHEEBin6jEnevyREJSsbsX9rAaEWTumeQjoRnig7uaLw3iYQAFTTg8RUp6D6W4j2eKNzJ3rV0RCxan9VRKda3SRLImI0ETHWk5dHcAO6mZ5s0nmUdGa/0yWgRubufT4Tjp3cd336zFPJNSpoVBZAh9tDbcc2wjAqfwu7s79x6Ju7vzNsZpWQ7H6c/+9C/evXlTSjGpZbHP3nzxFf/6N771ncfH+9ubk4/1cVy//eKm6pAAeZyPd6eimOej1d4fo3l1fHR7s14HXurVN53Op9vjJ28f1z6+/tWv/fDxz5/W9eu8Hafp8cefjMNXtdfb07mkn28+fHh4uMb2lY9e/+jTdy/PL07VfGvn08vi+MOx3crh4/P5zuS/+Bt/51e/+tNtu98ub1Z0L1VtHtdHRkiZzObj+RZlsvQRXqYJmWMME4s+QE8PpndPz4jE5boRQ01SOcaWHsXmlj4t09GOjOs0jtOWGevoKRDVMqInugSf52Z6CFNsTgIYZv8/nv7rWbcuu8/DRppzrvCGnU/8YvfXAQABECJIioQpgWKwTItUqRxYJctlly4t22XLLv0d9o2rTJftC9lWEWYwS2KJEsUgAgSJQBJAd6PR/XX64gn77PSGtWYYY/hiHeh+195Vu953rTnH+P2eJxh5BUcDrKAIBKa1mnoLVDGKI3cB1NwBrLWqBigccQE6WiktZ2jWZoOiLbdmpenDBAiBI90fbs78qlEbu0SOSq33njCqaooBSouxS6E5N0RSNZmnVhW73joCaC4AHUcJBE4O1YG1LAkudkR0MVNDQKGkAkG1LeBorUr4llPIMcblltzU0KmpMTf0AOaOii5OBopLFEy5AYEJQ0aqRrDwAx2pcHRjdDIkBRYkNVEO0CGDoZM2qRCZGdyXfU01LQ49UWpuEAKL5PmwWqd3nj7eDludwMtulqnRGZXoXNmgVFIymLOjHMv0+otPCHyz2WwvLzcna+n6VuzVq1cffvjB1TP7/NPP4bg3zVHSPO1+8tnnf+Rnf/6f/+o/Ot2k9549Oeym/XF3uulx3sHtxxnqdn2SlWqtTKnOZeiGbhzCCbdWaluli6G1/TpEp05ivLj4yFpJKaonOP2FF9Pdl/vPPrp4vns47pr/kYtnkOtR6/l5ulxvxm481CbdeNxPx/lwcdq/N8if/vqffO/sSc7307SbK5TJhEjLAc3cgCJL18+5BPM09pjdWhFZVqsNCN1BmLVArepiSO7FQhLzxXEfHsrdFYeWRiJYEZQ+xGOK0axa4ULC2AKRBLIMsytxdVMH4MDCRgi2sLrBmiODkYJaQ2tEgLMbWAZLRFmkBYqsCZndprnu3TXXMtUytal6dctgYMVbVgBQbXM7whyjZGeXAAG3HSaYGQQh+Fxn92QMLBqZlRLCrKr8/BeudKHn+XJXhbdez+UY9jZ5oEikaqrWWm1Nzb21ZoqIbEDubupozkQhRhGJIbCEBc5JSAunEJAWA54DmAdDIGZGdDAED8juS9XZdblIM6MgJZXoJkCJWFhEiNECNVYSZAakxsss0Lk2MJWAPTRus6nq2Xb73tNnl6cnq261WZ9ertfvbC4Gh1ryXEquNeccA+Vjri232pYyWK3luL+/v7t/8cWLUmYEYwmpH169vC7zVGq5uXsdKcyH3fbk5Jvf/ObrF5++8/jqfLv+9ItPupBS6rDOME11Lt1qPB6mrG2a565LZ6enMYSY+r4bV2mFKKcnZ5oztrLpuyTCakPszvs4mK3Mv371fPSwe33z9PTk+dXl7vrmm48eb4yfnl+drEasGpB79K8/e/rz7/3sn/zqH/V6f9jfgod921vxUkqQgAYhDt1qDcDLrY+DEEdQFMDU9w4AWkGLem5azZUDExDiIvBorTV3L7mcrM74nfPv2yvDVkHrXKc8Nc+Arl5VtWlpWiKgVlhyYZ2EgOJmTkIBGwGQUnBzXzg51gzw7eQDwZYwH7iq56o618OU98fycKy7Oe9rm8yqAy4LXXBUVVVA51q0qZsRQggxhcBMFCSgEZoBwkJJdwNt1mqZjzOffW0LpGALiKc5OL71SoHqMtxXMyNidzM1c6u1WdMlGOQOunC9EYJIiDGIpBj7rksSuhgDC9BbveLbHBsCYUQUYCJkIX4b7URoy5DVsJkaGCz6SjEQxIAcjMWI2dBcGpARIZIRmhACiDXURoQdOtfMOesY07Ory8fnl+txteqGbdqerU8fpZPQvKgech6GUSK3MistiMlh6Ferk9N+XK22p6uTS1Mt8+wGn/zk09PT0+fPn7344osUEjlMLT95dP7wcPPs6ZOLcXz94rMhdYRi2iLzw+svsRYMwiF0XXdze3t/d9d13Xa7rbVN8zQM426/r+b3u/v7+2tGGLpYao4ppi5YyRExET99+mxMq1DtfD3Ww3G9Gt7ZnCTk7Wo9Co/jWoKcsjxfPfngyUch7XO+LYeKMTRjnXO/XpmqqYc0zKXWWhOLhMBISMwSQY2IhKi1ZpqzVm2NiQGgLak0q4BIiKDOEDfrk/J4/BheuzUDLbM2qw5q2sCVCZldhJbyvVcGA3R2dQNEAkWvYMjWtOYyl9ZqrWBvFehL9sHUGUhNc5vVa22leM3e3qp73YioNfdmpuBKraEb2NvRY1CF2lqIMoyjBCFgAiZcnPGg1abDZK210kpuvHl/JeKMwgCqxdzJkRzM1dRKyaoLagrctdYKAKZmC8/bYRnlN2vEGJaLs1CMseOYWLoQMZIvXNYFeM9GxIQLqhoQFv84MYGhwsLzMgZAdzMzpUYBkQmjCxkHRkHHRY6MSI5EhGhvJ0/I1IFLzc2zCcLJavXk4uL8dDushr4fuz6u0gizzfd5GFer9Xra71NKBqCtChBxUIdWKwdJ44AcTs+35xdXp2eXaPbdb32rW3UnF+dlP20vLzfb4aTvt1347PrLmCIed2/2u/XqBGBupdzf3scUxnFszUKIr1+/2u33681mc3Iy5/zm9etpnh72u8P+bre/WyKyxNxMV+vV4Xh4ff2muq5Otmm7MfUucB87Nn50cYYIALaKaTuuu36IwKFh4Pjs2TNM3gpaZSXP05yIaqlg3vfDnOtcWuq62CUitFy9ZJEQhlFbsdYACQi1EQK625znaZpaK7jMngkdKMX+6snzV6v2vf2nMYrW3DQDqARCxFKytkZIQkKcXA2aQnNQAojEqNgWWx4SNGjuZm7mziwMQIQirGZWFdSbWXXPbW7QGlQjU1SDBRZnao4k0LAVMgUzWNwfDOKmTRuAx9TF0LniAvevrbbapv3c5jofjvM0leJyPOQYLEBEcwwtqUNyY3E3VW1a3YGBrJlBqzUTvFXkoQO6CbkTAQYzYxIRkcCCLEbCvLgaAcic3SYHguXaA4hA7qruho5qAAZMHSM1g/S2HFSrNnjLX2EkZDdEhACGaEiOBmTNkQOIIlV1J3A0F2Kgos4UsV+HYcVdCiH1gNbidHTT3bzZnm63W53ym5u71K0Ot/d3t/t5PhKDlpnc+u0WGJjD8w8+Wm/6J8+eb1bj3c39N3/2HXdfdYllhP28WkW3zIxFyLK+fvViWIuVu1rw9e3DdrMppd3e3pZS1qdnFfj6budWQp+ur19a89SFkLrdwz6s48PDblgN++Ph+tXraZqfv/u870M+POzuH85PT3PNIuLQpukgSOvVkLqh7ufp9qEe8js/9UHX8/625ZwdG2Rihzyb9IgcimmMMZFIFx0MqpMjA+k8gXsSRuF5nmtx5qju0zS31tSUhGqtbpic1TkMMZys7+rnOR9CHNydAieL6GAprDYrhazHGRu4O3EICQjNMnhDNXQIiAYI1hT5rfRg+fKAKRouwygDn2qFCphQycmcWQkA0aMQMphWc7Fm5m9B2apqzV1ZXUHcQW/f3LijN1xvVlFCN/SICDaDOhO2UnPOtVQengwsC7pm0R6DqTuYqdW6VLCWerbWWt3fOmAXiMNyaTAzBGDAKCEwM1OUGDjG0IUorksegYAYnMBxCXebUVM1B6Tlaa8AxMDMiNQAlg8zGTgysICLu7gpQo2svMBTzcQrgrIt2Wxza8DM0cXQjVCCPL44vzw7G8YxpshMzpUyDKW/PH3k7Oux7/uRieasKEF1Ou7vCT0yzftDazM001Ld9XZ3H/p+FYYU5d2f+eD+1auLYTzU/YSN9nst0/rxYygKLrNqyZNrVW2d8GE6fPHFl1r19OISnKcpF1MMuFmtda5Gcn23U+JVP5irA5j5w/1DN3TPnz7b73a73eHu9o7cASD1aZ7n29cvz07PFLy2+XA4vn7z8OTpk/c/+MBbe9jdujVwnubsBtx1KDSrhjQMcRRgQ4wkVBWJWFbCIla1ac0VwNxhno+1zLVWVWcRJGq1BIkxRG2V+3H97Mm39j/5ZP4S3VtrOVdvrTU1sOZFYSK0ZrnUXGdtFUC5tMXQsRx2FxKmmrsqCggSEVgQWQoeggERnI0jIr9NTi7LVFuSlABqzY118QMbgZmqqQKAEMkSy29VW1b3BZSppWqrrZRaci5zsdbyNOeceXg8oHEIwmSI7mqqhmjoUGotpS0zfTettbg7CyGigbU/TLktxw9wJ2IRCSIppS50IUoILBRouScDqDkAMwkiV61uimCESOIAigCIYNCA1IHMcOGBG7hjQza0CCpWFRXdl76Fuy0BPCMkNzBzQSQ3ACbmrsOzk9XJyWm36jAhc7DsfRk/OntfhPqxA1uyqGjCh+Nu93DLxCipNYjdENIQYgSDedrLmHIpkEuZ92ePT0+uLl585/vbq9VU9sc3t7vD/uk7H97v9mTwMB1A69XpyTRPuWRweHN352ZAWEo+OT1VMDeNIR6Ps4LkpqmLAsAkU82XFxe7u/vt9qSLw+F4/Ozzz9wckVZDz8xffP45mKXY7Q/HY8kB5dHV4/e/9iEbPtzfFFRQaAWOZe77jmNnVbs0mLG1JaIjSCFKEo4UU0zdW1p4a7kUtQqu8zznnFurMQQHA8cuRpCAjCfby9X77/7zL373h3c/wubzNE/HXdOp1lxaVjNrs6sRswNq0VpanmrLaMszrVUFQKcG2lpj5ESspoJCIEuTTCwAOrIiKRgyAAsiO6DRohlfRrXNQd0aoZIrL1yKxdiCuIQzxMyXbTGzoLs11dbKnLXWkksu+TjveP14zFkDaSDw9hbqpS030No0Z1ADWChl6m/pxO6qy3UZ/a0rGqo2NxeRlFIfIxMH5shEiVAWYaE5uptqdVNwb9qMzAncaYk4uzMgKmAw42ZNvZgLYgBuDIItCkprTRugiVfzZsDm1gAMHQlECFVzYImcKDHIvBnD48snwyhZdwbMR0nH8OHZI8NSSp6n6f6wL0crc3m4frm7uTW31K1QUr8eVquTfa0xhPPL89P16sn5ibMC2+0nXzz76Hm3Ha8//Unv4MgWYhzCxfnVzcvPSn3oYhdDVwEOh9lay01DJ/vjAcj71TCM/dmwOdzvHg5HY+7HbtWlLqZXr1+fXF6WmomQQB52h7lMP/z4e++9997Vk0dznl7fvN7vD0DUDePcKiIj0dnFWUWfay15DiC16DTnPg1EcjweSRJDWOQTIXQpDSzBFZC5C+zI3I0ssBT6EF04lVqOx0PXRQmYcwkiKXQqYgCPto/x8cU//s6vfvzqYwZuc2uttTbnkkstWt1qMVNwcAXC6MB5mr0UzwpGzQMygYvZ8roHRkcmNjRFMgQFdmMGJFls74CKizbcbNE+mKo1BV3AmELWuVJTczMWRlRCQicEhkUpBQhITJhzqbmgW82l1lbq1Czz6smo3lwBjLxhra3VqtWsojdEd3Tz/+4CYljd0dHMrfmiH1VwRFiyNDHELkRmXqpuKERhGVySI6i6KrS3hU+3RlW9tWXMissbcKGkqhkSlLf9dyVkcnB3rS6AoKQVTSu5mxI4ggIDMQIhIIOxBsZErRMKoRvW42YVoHXT7XwBZ+9v3mllPsz3r7+4znOZDof7u92xHEs+zIfjdDyqVq21Fh9PTr7y1fca+svrl9shbdZjSkygtRxeffbFuz//0e7Lm3q/9yE6+u2L11fnV7d7uBh7En29351uz6LI7d1tnidzBUjb7RUAMMH5xcXt/Y2p1urDdlz3/XzY30/7oe9a0dPN1fXdLYq3XKbj/r0P3gOXMte7u/vdbh84EFNKwsitFLOsORsQqB13u6lWQgKmuebg1FACIHEEACBVQnGWJZJlgO6CQMwhxS5Fa15acW8iBMRlrgww9AlYDJVZnjx+v67Xf/03/saL21cjjuoFK5VZtbWmxaz58og0aC1rczBmCaDumayBcg0cFMws90xEKH0nQEQkiO6KDIt3yLA5OjkgRUQ1VADEBoBk6G6NtE/YM7EZgioAOwEBASbEgOBAhkIhdFGim9bc3Fuec81qoK1WN1Cdef1kRMDamjcz81atNXNFM9AGZooo7q5N39YRG4CDG9TWAMFMEcAByABtWRtTTHFBmTGzxIDkRNiaLyQTa6rqVlvLtRUzR4elbLCETc28IRIo1OreXAxBAR3dlgQqgkUC1tZaBTABqEzIEAkU0BgFUBi9k9T3IB0Nq22MwarKRI/iow8v3sfmr26vH3a7w8NxOk6tTqWVOs3z8VBKa1XBfBg6owqa33//K0+fPn7x8pOHh5cLp5IR39y9qK/vvvlzP//7P/4htiyIty9fteZnj55O+zcxSsnWpa6Z3d3c3t68jiEihe3JljCEGIn9/u6BRUSSWlv1aX+/T8OQutj3wzSV17d3Z2cnX3z5xaPHj88uz+dpOhx2x+PxeDwuQcWqJjFuN5v5eGCkCHG/3+92y++MVRvx4gkmFkEAc9AGqhhidDevhuzC0ozVlBG7mIC8tqkViIKlFXONSZhC4ATE/difPX/nen/71/6r//Su7KhAybmWOde6n/fL8anV2rTOpTTVVkEL4sLpNdJmVbODgFUWRLYgkliisCxHFlocT9isARIAAQJwBdClTL34XltVQUq08Uaqjk7gVs2QlqqpoAMiEIa+6/qhZ2Zh4T88PJU8Lzn7XHLTwsOjHty1VCuNFNTUAAxx+bgvXrG37fXFcLU4Is2q6dLVUnf3P6QumoG7MAcRZiYixsV3Sfa2Be/u7tVaU81q2b25mRooIoOjQrO3XzB3Y3BAJVzqYmZoAI1dXVsxrdbQl7MjESERIhMxC1NEQonAAceON8OKu1jqNBzOzuT0fLuNLPdv7qwZAh0fdrubN6W0NpdaJlV1ddXKgs+ePxLBTz//yeWji3c/eO/LLz67u3mVpymETtC+/OSTh+P9+x+++/mnn3XMVvMh7y4uN3luKY4Aqtr2u/vd8Xa/u+/iNnSBGE9PT8281lpzPh4OJ6cnZZ762O32D5vthmN3nJu2Cm4phTdv7s4eXbl7SOn27vb61YuhG0qZRJIQp75brbfq7gj7u5vXd29OTk8B8ThPjrAsfg1EHUrOahnAU9dVq80a9gFjUHJOCObH/f6w3xNhjCzMYDjPR2QNoU9pLFYCpYuzs+7589/7wR/8yj/4zw+HuU06H/L+eJhreTjujtOu1ta0zaXklmuptaoW9mb5OM/7plphQXy2SkwoFAUSQYohMRBpILG3DUFVdEcwqw6NgE2XvKaaAmNkT7Z0n3XJnREqAgWB5ItjCUgkhJBiCIGZjNwVYAkjw0JXaFVbbTw+HtyXXtRinnR9G+c0X1paujCOxHzBdKk7NENwNnMAMnVwhMXhupj3mEMIMSzvAWMUN2iqAG7NTKGWptW9Qp2XEpCCOiE6oKmZgSkuhy5f9mfqaEBLGVrRnFzBDQASkMYgLMj8Fi2DDAvHACMB4xiGIVFMYC291//0eVyX48Ppdl2rmSqg3715c3975xURuHme8+wOCzA4deH09Lzr+Cef/FArPLp6VOZDyfM0Z1Rv0L749Edn52erYfXm9fX26mx3uAvcrc8vtc6tFqFYpt1c93muudFqveIQxtV4PBytuVuddvvYxRjCdDwWrV3XueGUSxeD1oqKw2q1Pl231iT0d/fHMmcROs77rh8I0M2qgZrlmu/ubvr1ys12hz0HjhKEQ20+zbOaW2tuQCDW6tyOtR6ZiZAd1KwgeUxSW51LYWRH3R+nnHMSWa/PAHF3vN+ertfrMZ5efveTH/1nf/+/2D0cS673+93hmOdpblrmPM85IxMwNmtg1sxBQ0AJIHlfp0NWpcWTJYL9wIEtBQziTEBoiRIYNs8Kzd/KYwu6qxv4YgsnJkFldyBAdUIDcBJEAnTCQOQE2hqzEDESiTAYuJqZLZ98dHSzWlSb1VJ5vBz+cK6Ji3KQfYE2oTu6oy5PGEQ3AEcwdmU0QidQ56UlAOjNVX25bsiyEg4iwsy49LUWzXdrXou10rzW1nSa51zUFdzQ1V3RCVprAAgQW2luiubaHNTRabF3EwuSMZMEjj3G3kSYOYaAJG3RjiyOFo5CKXVJuph87r958s2vXL1Trh/mPGNHpTY2vLu/rVqscqnVPEcJOc/CHLquH1cYCc0j2pdfflpy2Qwnanp79zJwXCDzn794+ejJEzBPXZeGEX2MKd7fXCP4OAzTNE+HKVACotXqdBxGBFkoRiXXVmYS7lIy1VJLjF1ruj8eWql915XW+tU6hMDCx8P0+tW1e5umXdeHFId5mpkZUcCxzXncnCLA/ubOTDmEFKOWVrWu1+s0DK7uagA47adaagqdqJVpnx/2bW7k1kpJEpiUiY7H8vrNyxTHvjsh0uNxh8jvvfOUScKjZ3/3N3/tb/7jv8+UoJDWXKZlJdrccvOKDLFb6mYoFCKNEYI4i/cENM+5uBozs/Y9dl1M7MCVCENgQQFwY62amRjIGV2QKTCSoSk0cAU0Y0TGIEoOSYAA0MjYoaECIgIxi4gw0yKnQjNGRsflHOgOtbZcqjmKu6KZueEiHF/eA0pAAQytqQQAQ7NCDCEIsYA3IkAwRNQqgLiQ+305PimZYXOvboLgYACFiFiQGwp7QGN3RCO2BarixtagoRsqMzqhanPLakqAAM6CrujGBIjiyMpiIswdIrMsjui33rkQmVAI0IjV3XKdc9vMs62QBiE2GDar4+G+lBolNLYhdnXczFjnh2yqVtp63AAZx+gAteamFgnXQ7i7eWklv/POUxZ9c/16s9nk3SwSP/300/fefffV51++8+H7ZKnOdarNAWqtrl6rljl3Qx/Z8nysLffd6KCHaY9Yuj4whVe3rxqWUQeWsTUrOg3r1Xo1APKcWyvTfLz3djzudjHCetzWVuPQbc5PteFhf7cZVxVjmw6EBswAdDgcYxovL86Qh+OxIQfpYqCgylZKihsOCG1GL3netTpbsyi8WfcPeXe3K33cDuMwjsM07Xe73YfvvscS0QdM4bvf/67OPqxGgwrWh0BsrHl2atyFXI4yc0yDIwlRYgKj4pZSGPuxlHp73BnB4dg4+rBCwIbGiCbECIpRSS0Qqzk2h4AKxkCwaKLB0U0kkQdwYBHQsKzQiASam1Yziym0aojOzK0pGyxzzGWl7VYVsFXHhoiwzAOczBDR1dwdiQ0cjgZBiAGXkrAaMYA5sAoT0iJuJVMHcCB8iyNy1Go1N2+uaqqKRABNVYkkcqjYlm7n4qwckllxbQwKxuoN2gzI4tbMMgkDIhAhOoa3d+NG6OxdCqkLGIjYlhHC8k4IEkIgFnMGiMzcyKFVmvJMNr95fX1Oq4NPGGDQ8DDX2gqHQNINK6o6TzuccpHA6/Vqmme34o3neVJyBxf2kh9evoDzy3NVvb+7XQ9xM57ePDy8evXy6umzV59/+dWvfjXPYIwUA0KapyJCD1qphIfdtbqHkNLjd3Jp7hpjnOfjyy9fe3MXzbWIBVXrUlr4ZfvDg2Aisnm6nw4PiBRDr42ZuF+tnMXNNifbm+s3U23bcdicnUzqpdpq3Mqwas2n/b5UI+qEY4p9vzoNCGpFm0lcKe6b74PEYXNhWqdSH+4Parg5fRSCAWgpCiDDqsvzYXX55Pbu1W//7j/jDkOHqMAxgmi1zA7B0/KinvKxGrLAOg6AVZXoLSoCmZnMy5zdaC92stE+0GI6qrVGAWYAUyYAUxAGIUSzpostmMgJlREijBiqeSBHRxJE8KYMXrFqc3VCVFXmJWKvAam6aXOAttBIW62Izoi8Oh+hGTi6m5shLslZB18mk7g0HREQHR2IMRLywuVxY3dWNURf7haLiU1EhtQFxkAEuoxTCQxcUSu1giFIpECObq1UzaXZEhhqxWtotbIBOhMCIwZB8+Xzzc4OghIpdkDBJHEQIW4Oimixp66TmCRE4Ags2MnoUhEkEbQjb9r507OzjuVwOCgCA0xzyaa1HlptpWQAXK9Xx7xjIXBLLKvtWGstJdc6EzoRTdPxyxcvNuuLdb9SUAF65+nzw3Qss8bY7Y+vLYdx88jMwI6GpgohJJbkaA+7GwNIcTBTcETFnOfd/nYcOgk9c9rv76c8r1YnMVCtdTpMm3F9PDz8+Affa0X7ceA0DP2IIobUmhPizZtXtdTteq2qXT/Opa5W4zgMrSl7Wo0nm/U6piFRnEuZplynfDzs8v6Qp8M8H1tuqKqtHad8e/sGGq43J0hmzY73u/u7W0nh4tFZa7Z69vy//NV/9H/9e397c7EaJFAkEmUxEkQh4oSEEsxQW3P1mRD70IMRmZCyKR3rfCy5ZRcwII0mq5CWAXsIgdCcDJjcDdGcHQg4SBBEVCQKzkQQQkwQFxs4gxACCiEbkDiQqy8YkUVFYGrQzBpqafNxnudccq21mTV3Y4m8vVy7o3kB8LefbHB0WGJ5rqYGruiGBCi0gAwdMQKKLdFRXO5jjg68yE2DpBS6IH/ocgIABAdXXFr5YBqCEFJrNudZm3tBb7SwI8EFWQKTEIC4ITEEQFjIREEoROZAiyYRwqJCtdhB6lLooiSN0T24o4fQEIMV7iUkGC7l8YoiuDHB4WHXqpJwrW3OVa2CgzYNwqdn2xhZoledg5AwutVaMyEyUBJJiNd3r8d+dbE9z1MBhCePHt++uT09v3DMx125uHw3DuH25lWfOgkiklqzzeb09v7WVOapnV+ddl1XjgtxHgyrcLq/fwhdXK9Xq65vtd7vb63hqh9evvjs1Zefb9ebYb1lTin1sYu5KSJrnrzV9WrMx4mQplKK1tStm6KkyNKFMSqqWTtOh4UalqdjbSaERG7arFV3OOyn/XGn7mN/IklaadN+Px33brY52axXfUjjcP7oV/7rv/Nrn/zOarUdjSmhgQoBMQEgsCEDCysahEodqHnHseOVNmZlUCuTH6eiZXZmUMbWJKFEYkCAIoGICMCdVAHM1UEJfekCCkJgCRB6WiHgMi5aFl3L4xoUmpmbAgAiCVHL1ZtZBVXPrdRZvf1hxpI5hMCM0rwYmLqTE2BENHdHIKvNQZkRmQDMlFoDZiY3JzRqhBGX2Kg7GaG8XesiO5A3q1kpUEBkBTRlEQE0CjOaegNBjn2XSu3n2upkc23uSESuJgCBKCJJMFMwIHRncQQkQWYSBLQlV42u4IsFAzgG7gQZlFUoNigTHhNyoM734Ww4W2Gaj+quDC3Ph+OcY+i9aQgBYSzzHCKZFmLvEjOvjtPONCPZnO9YEoCpH63yMIwD6/3DNVF5+s47dw+7V69effWrX53avIpPD/Kw338R+nG9usi7h2G9iiMf23U4GVbHszA7oBUlk9Sd1ftXVc1E0mF/v99Pq9VKJFbHm9v9dHyz2m52d/uyP0qkftgQd26FA6X+ZF+uQ8T9rER8mCYwz3mubidn56atlGmaUNJgEwfedP0Qe6rHGmJHCMf7+9qw+oSqVqurzdOc63x6cpHVqBmyz3VGs367HjdrcuxPTmDo7g65EwlhQhJACQJv0ThaGQjRzSuhIYEaqbVdPrL07tHMzWtk6EkmDFgxJt5boX0j6qxjIoXsoQMjZ5LAzQxFIjtQW2oqCEgkoTkaOC3NQXzLDWxtoZGAIyAyLakz81qrNiCSWhXQHEBbJVn07kBEPJyNy4iUgNzVTd/ql5dfZghOTAsgFd1ciEAQCIiIFsal2bKzEEJhJsEQhQMziUgCwJKbO4gEADRd6LaIyERijqXUlkurCugoxJ1TRE4kAQQXAuqCD4K3fw4AiZidGN/C440QAQgwgEQI0YmVg8QQGMybOyNj3NDq1AYWNrDd3f18nFCk5Lnm3LRpzTGAkHaRAGprC31/US7j8biLYUh9Z+AxjkhCHLcnG1fb3eerqydz2U/HPYZ4uX10KLM1K62WnMmbESjg9vxCJJRpSik1KwTw9PLxw/7m9vYhxBiEa52nqWzOLiWkm5u7T37y8UrUqBtPTnZ3b3Ku/Wodwrg52662Z7c3RyLLZWKO2nLLuWg2h3F9oc139/et5VInYUzDpu9Pq6o3RcX7N/eHu/vpcGx5rk21OmgrpeXiuboT9sOw3Y5NS3FNfZ9SWq/WMYXtRx9e3zz8w3/1G6/0DVMNURwJ3FiAAyMjLNyDSM62zOUN4e1Av1FdfEfsaJ69afNgxETWKhNSVJEACgbOTMhhKcMSy0LTRmAEcCNAXmJg0NQNbTlWOLS2wB7UFtWWE6qbGfpboKY64FthtgG6YSNhFhZQJyR3BFfwhihL9B8BEHhRGmo11ckhxhjM0RWcPKCJuxK8LdUbmBAsWjoUdHIla75EK0QghADO2sTNESoighMBC3EI1PVQFaor8lv1gCCSICipqqq6GhADAC49AkYwB2BHNENvgGxNCzrEFJGMSAkj0xpIj+Xhfjp8VnwVujhIPZBVDXFAUork5oEggnSpK5VMG+Aw52MphYjUKqF0wyjCQ79qrRv6bQjBvAzDit1u3rz64Q++8+TJMzeuVUuZTtanDw8PxPJwc73tglULHfUxzsXPLt/ND1++en2bYj9PB8gwjmPJU2BACoqlW42o+PmPP3Y7xu7DYX02bk8gdCSpW21X/SbE+PkXr90khRqYDvtdqYcoMVIyDPM8m85MxpTG7SmgsGGed4jccrm93u0fHoTY1M2tqbFDbrWhSugjS+qHbuBDOR7n4/psO2429XAkU0lbD/2L4113MTzaneY8mLWaH0p2ZBB2b24O6ORtOVUqoSl6hpY5kkQXZEJR741W1am5q0U3Q6oOxVyqUYxamzMGYgQXQSAsTRkdEIWlVdPaDJgcqxIjgKErqpkharNsTYgBwAybtqYN3YmEAB29lWUqo8TkgKZqZry+HBDJdQFuErioooP60jInMlMAZ6G3EHNyW2667q5qgMsQiIAcQGIIIQSUhXWtBrW+PZgtuIm3YSZ1BCKnWkotxUGRyNAXjx/6Usx0FDIAaMaObydNSxV+IbkDm/vbwKo5IFJwSRQ7YRFCZ4kGVBVJTdV2U+2xP0tnQ4y1TOYaFyBSjCnGzWqFZDFw1/chhJA6kWAOtWgIMvQJEWqdJACROrR5t08dp767OL9AEmI8Pz9PYd0QUkCApurk+HB/Rwyb1TbF3gj7brR6/OLzH509fsxpvHlzw+StHPcPu83mcr09CcxffPrJ7ubl03feffbB1/thHAQ0z9vztUQJsXtzc8sSma1OeytzqVOIsRs386G0NuVyMG+ttj5tDMLtw5FQ+hTM2mFfhNPp2XlbYu8cTAs0rc0wBIAmjONqfcwzp3R6cYYIKtgP4eLi9PTJsx+/ePV7X/7+97780fXxvo8s7CZCkQUjAKm5gkskYMPgTkYiBupg7hipS4iMIIDkCl7VXD0UdOwicgghcCJkIxYHBDARQsTaKjL7f1eCqabqqOTuWs2al4amoGBzy7lmfysZWH4atJlWc3dCdEN3LGVGMgnEHIEI0MTs7QgI2ZGWNiS4s8Nb7gqAE5qZ0ML7VIeCDlzBYCn5EqE5i+MiJnVaEj3m6Kbu3lo1barYRWdBtUVSSabNzRiZKXJEMTNf2K+AzR1BcVFwM5qjGyItFWVD0OyNFAjNqLXGgg7AibT4PFkHIiwIagqtGXhs4FW8WclV920/9l3Oec7TyWYrIbp7bYWRaxWhAJSrNsYANnV9cLcYh/Pz8dWrF3XWk/U4jmPts6p6ba/ub7eb0y4OV5dPjPhQvM3Xiwp7CP1EnYJXaJuxs3lutUrsOcQ+BTOQMDAf7/f3h315/DheXZ7/5Cc/ePX6s9V2e3b+7OryZFLL97uhGz16a/k47TfrE4Cyu70+HidAHjdrQL6/vwUt1Y6mgtCNw7Y5TPe3oV/FLpVW57kM/Xq7OX/1+ubufv/s6ePZ6qsf32OrsR9Cl6bjzo13dwdJMIQeEaqWNQ7BIfWrncg//sG//Par71/Xa+i55dZ3HWjMxQzblDMxECAgiIiSIjsYRmF3q1WLzhGDBBSM0rwNoRlVl1IRQdHRjLShBEQmdweCpToib+uHCE5qy5rJHU2QmmkrVa0yirvOmkGNoxg6mr4Np+niUiEDtaUziW9NFMvpuXkTX4Kp6Iu+mQXQoFX3hsgE5u5QGyDb0txRa9ikmLoRMhEBoyEjMQnzcolVdCJoRkQOgKpWi9bq1mxxPCGCN6u1NNW3CAgHfNtsRARnInCACuBkarWaWwNcCt3gTrU2Jwfk6qBmiNVVUkdlAubGyBREXQ2CVRdMwmbFmCIRmFor7ezsnB3JloySaTORELmbpoNaq2UyBUBLcWCWw+EgHN959uHtzV2eLQTbbK6slfU4np9vr6+vb/bzarP+6jd/6m5X7l7VeXpT6kyKFxcXs2dnUp/HYZWL7R++zMfaCsdkTy7OfvDZ9Vz19OyiWgXiuRYM8uj5Byen51ChH9Lxdj8Mq2OZyuynZ6c393fXL3647ruLiyuX7uZ2p2XPUKZ8cNDt+lw4FW15KuNqc7I9c1ensB5X5Hx98/LL1y83Z/3zj55/8sMfowOnjgLnecpzo7GLQdaD7O/f+GQnF5dnp49JS9/339/d/uD+xSt9sOgb7yfGqRV2kA6yEkaB5kKLGU8ZuDYVIVBnjJHEa/EIFINQBePgPBLMRdWaUJNABtoacEURImFTr6CRw8LLIQ+q1pq5CRiYmy10rNJydTdyMEcjQLPMzLTsrszfelt0odZirY0FRQIxm7uBgTdhInB1MCImIeKmqgACjHmuy52DCIUDLYFUEVLzas2X/bU6LM5mDk7uCEtFzY0MiMicCDv3eenTmCOzExE0LaXMtTRtS+MRwBHBmgEs9xRAJ9dluwdmS22anMi9qS/bh7akhZpbc2wlzJMqgpG1pExg6qLY1BR5E7fn68d9P/bo2Iq2HMLQSrVaG6qga1FAJfSmRigc8HT7Timzup1fnBx3bfcwb09WMfT746FOh/XJ0MwenT1ziJ9/8ZPPXvxofbp9/s5X5/1hf3cNyMcyBSYOm6VjEDCWdiilfPTBNy4fPd/N9w/TdQhpNZ71g1xenTEHAx9W68ePnl1cnhWwgWOk3MoMRmnop5Jvb16GNDx+56ultc8+e50kdJ2YinR9oJCnejjcAVtMq5zz6+vPScZxdbKrGRTyUS9OT975yjs55zKVfhhLK/f39/PuIXQhYTL3ac651W6dXMH7frM5c8zT/m7X7p0tMmVVRCZgR6i1OSEz+/IAhboofIQJmSQkrDUAJKIgzOiRW2OXBGxG2CA49WJoDqqOuSIJiRMisJlhQRAhUdeci5kTLtNPzbVqdS1aqpIz+IJ1MArOKSBzyQUAUJbqlgIsdhJjJFj6jeiRWLrEw1kkFEThYMS6ZAqIAJGrTq4QkGMnsSMKlRgJq1BszWqrSBgwLMJUCMBCTEDIwnHp0QcRpqQKpk1b86pewD1YrYcyTVPNc4aSAd0BvLWq0Cq6B0dTbV7RZ9QCpVn0Rspg0dDRFQxcmRTcKyiigiuDBwdFRAcmR1RQq6i9VZx9kpbe7Z5ebh/3wxgloHopxVWFQCKjE4Bra7kcW6uEHlPHUcZxDBzd48nlo66L834/DsPl5ZX0q832dLXZOtvZ1RVL/+b65s3rN3MuqzE2a3e3d1bz1Iph6lLoOiQKu9vbaihD6nvKx+mwP3aSAPD27nqzXat5bXZx9mhzcc7C42qdD/P94YubmxvB0Rnu7m+3Z+uLyydN6cXLLzfrSATFIcjYHOacD9N9P8au36TYm2KKkQKruiuBV2/H80fnNAy3L9/s3zy8eXNjVsGykMZuMEpeZ25OKaJWbP7O139qeP85FPutH//mt+6+EJBatJpZM2xQVdUc3M0M2ZCWPqsREpGFGAMQv5UfYhe7LnCy2tpkBq60n+biDoIk3AgsIHEABAhErkFBfaElo79FkwC5oFHLps29NDdaQsuGZua4iDP8FzYAAIAASURBVPZY3FGQiYCJQgjmnmJPiyYc0RHNLPTMnYzDIOTGSR3JEYn17aaLMqGG0OVsIBois3iK7AhuIFyPB1Wl5GBaEHGhwqkCeDADZCLShcXHZCLkMbmDljqXyYoKoGuec26mxBACm2lrBRt5baCyHKya6gKUYKCGjOjAFcE5OUUu1bRhK52DMrOq53niINkIW8UGmVVcMLQ09oNdXfHqncvHp/2q5iNUnefZ3dabPgrlWtkRjICZg7gbE4cQCFgrADJi1Tptzy8eP3/vzevrh+yXJ2chxOqMzgTD1z+6ODvdfvrJl9/6zvc56te/8oHEeDPvIg0dhNiFpixscy1M0vdrB8s596m/uXlDLI8fvaeFKubHj560BozUrLXWiME1prQNfXco03a7XUv3+u4e+/XTp89vrr+8fzgM/apqmY/3tdbtyUUQOhwOCHOMaZoq1SKs6jn2YX22ztORD13N0+vXL6vVfNQyPQxDF1MP4JpzizzGbpqq2dStRwiPcWvUBABmz7U2b2iEDQwAzSo4SaBm5ggVHJEMS7PSa8ciixKY3QUNvTXPgNbAmzd3Z0NBAmjIxIyARQ1AgZgaGRNqdW0sLAGWGLV7BTM0RfegWgjFya21wBRCEA7LbdcdmclM1doQO8Qwu3M3LlNRDC6dDOsupiAhST8MLK1WAA+ITpEBYm2Z54iIzQsHSR2HCAYOxmqqbozR1at7CAHMzFybt1b7gYkViBFZ1dGKhE5CH2PUUh7uHh52h0kxmLeWAUAFCNB8Cfijzoom7giA5qToDhqI0dG0EjYOOESRnvdzLeCu0Bo2VSLQiWZXLU1n1yI0eCAreVaQR8Pmw9MPr4YLsjZPO6tHa8rMtdbt+kQ4zg/7P2w+OAfpU1drE47odGhTYABtu93OMF48fhqjgFWRWKfD61df/Oh7D5fb8fl7737j638kjqf/5d///335xU9+7pt/JKUBIXFi8zgXDWyAKFHG9WreTe88fe8PvvutWvPl6dnp9qKWeX94uLp68nB/VNUxDmXO4gjeD6uVa+liN47r6VjXZ4+61eruxefTfupTKtP+eNxzgm7oALA2H8ax5Nx0yrNK6Fh83Kz7PppZ38V5f5h3h4CQ1cpxxw4c11m97m67RI2SG8ehf/L4PZHkCBBjwL7MeV8ma6XkabKGalBrrYquUQAMgJdWIKhbNU+1OBMbkLKICBKDmqkbIi/VFleACGiETEhuDq4AaObE6mBuYgREbgAWyMwUrKkrabNFSm/NwDFK6rpIRO5L88cZCcwAltyAgGrH7uQARCFQBEVjMaEmsYtRJCRLSWoN1QsGNQVClgQ0ozsDW+yIGZUcjb0JcLHSTCMTKbgsI0+rizGbeRFYL94A7oWjsPdUrGvoudhhdzzkYggSIARGap6VnER1mslBkRoiAJIQuaOpk7u7CeN6Ffs1ObWVCG5CPvrx3msumtWaNHezUit5O/gRpZPNOKRaKD+Umo/HIqSA2toM5qv+RFBuXt6uhlECHQ4HM6vF+r5njiF0agbuXRqstTIXb/sQQhGoB719uL+7fn3/5vXd7mYuVfeH1TCePnn/T//yv/Hv/ZX/yV/7f/7fSv2dX/i5PzFX6S7PD4e7MOsg0d2HdXJozHzcHxz99ORkf7gPoRfC1TC6WuqYOORZYwQ1QR7m/GBegwy5tM3l1bg9Od6/iVFWm+3DzfVc7imGzbjuuv6wryF1WhuAMXoklBCk7zkEa55zEcole8lzNRXiTHm1uhg352/u3jDY+uwEKDWC8/Ozq0cn0/ymg+eueLe7u7++3blCLa2U0oqVhq7QHBlqdkBxqg1mN8LmSDJrC1J7IQNaan4C6BSVjAkDgycxswrG5qgNkYxRmLS2ZsAsgLgIdJsqADUTMgUSbYgk4GURRy6DluWdvXCqzAwJ3EGYYgqMjgRdEDBQxjj0BjZrVlVDkJhA0hQSSpSHY/a5ITqRASInYLGldEsELIbglWaCUQRaNVdwNnCvTh0ZggVZnGfNAQAtpY6IYsBVnzCEbI3FSb3ppAxeDQJT19hNq0ogqk4RrCg6AJObezVmBhKAgoxpJeMpU6ruvunXtTkU4xVbitNhzqW1uQyS+r5HVCg2dKuTuHp/e3K1veji+thK58VyZZRu7EQiEVWdX7160Y8rACg5hxiZgykJRwpeyqymQpGS1DbX6fhmnl6/uc3TdHiYfviDL764flPdhNJ2pOH1772+vv4Lf/4v/Xt/+a/+9b/1n61/9PLZe1+7208dceBQa0ak6TBfPL7Ytfu7+4d+NZbdcXNyMvQhBNlstkREkZc6Ewa/2eXqluu82+3OLuPZ2dV2u57yMbAi6N3N615gHLbS9UCJOXRDiLG/uXnd9QENMRgKuNaWo7mD23w47veH+4fbk6sLm0voTk63V4dD1nx49s6z6o0Yzi4vt5v05nDfv/qyf/417M8PM97f3rTEZWo6t1ZzrZmIWMCNCAMlcZrBKmJAJ2xYtJTEIBEADNQXCj8jYSBwZqRA6ChMROC4xBhgifGALqVARzMSAwAD1mZmxRqYJUQWFFV3VFgU7bxQvaqby2J0QQdwEYkSaq3ELMycBAOWuRGAuoOC9GshyRQwdhKrllnZAUgVkZkleIOlBIuAi9BdgYuDIXQLv40dAA0xMDOSAnpTR/YYZeiTOKbUh6GTGEfEbd91HBqXtHu4ebMDLhwMUcmANACFXqtlWBo5iuDWgA3ZiBgirR+ltKkUsY99oLR7yEGc0Su7GBmAAI6pW6VeUZyU0YXEa5gPJcD+GA29g9aEArg44nF/KPNkrd3d3fWpI6IuJUAnomWi13cRAFqFWb02f7jeU0wQTz/95M13fv9HD5M2HUs9TPXYbo4XPd3dv7i/+1t//i/+uV/6E3/md7/7/Xe+8vXj7gB9fHS2vb17VXJ9dnlZj9mtYaSocNDWsnWnqR9iAz8/vTgcdqnrKZCZ7fb33uoxFyLpujisV0Xr/v562t3evHxxsh0lSuBERLPCXDSmVFVDiikFByl2LPOU2K3pkmJszQPJs3eeTlmz1qur93a7Ns377cmKgrQGlxdnMYZdnjbhfMtrbTcUQ9d1qlonnY/Zq1nWPBeMMqTInQOjel3wfQ0aLFizEIv57M6WyYBQSII6IrqjQSBiEEIWYGaVhUqrZhqAEalaAwABaU2ZyUGJ2RdvkC8wqYBYEdGcUowsrjYjIS3DDFViAACzJiEuz2URTwEQzYPXbITg5MI9SggsGjuIhWDfFBgRmJTFOIBWd3clAzRADxzQhKg0qMBAwGDozQAJIyhPFKQZoiuxExmHNPR97FIIIYTAuE6pN7aXr+FQHmbL1omICKmU1gitbzQSIlR3B0ZOClpVCXw87eOJDVvqukAGrD4H5pihBkGn2gLKanXSxRRCGCg0wYaH19c37TBdbtZnx6Me+kdnV2sZhEIDq8ddOU5lns1aq0e39Wa1PRxnhwrehsiITiQiwhIJGdPqeJju7/LHn3z8ky9ff/DR15+O3XE37x/ur3dvplz2u/IvfvTyk9u7Xfmv/s1f/tPvvPdkbvnJo6elHu9zPRbd9oOqgqogJUmGZTWMIYRmmrpREdRssz1v3hw45zoOJzevv3zYTVdXF9vteS7aTGPsmgxPnnzg4rMXUEHzFaDpQ6u15rzqejdfeGfVwcoRO1H3iIFTZCIHZciPHl99/untfl9jjN0gk9ez85OL01PluTXpA3jPHNf3L77/L7/7T+firUKbqjXFBuiUhBm45RoGQzLG0KA2LG9b6dIaQbXgRtTMnIEpAao0NHSEwBQYFRVIiSOSsdAy0DcAAQCnRQbtitoKOGmDZkBkiEZkTRsyJAmLVc7MQgiMwawBgDYFgBZabVMUD8yMpjXLsgjA5W+5hAQiCKgugXq2CKTE3sxM3kqnmQjUGwcGgCgEFsYVcTCo6guOC8C5kbBEdkJ0V6215iqRpYsxdp2IhBT6GLo+dhkOVctR7+4OhRCQQ1oLV9vtNFXiAeOKiKQclYgBOFe1ALHzYY3jKhAZNSBQYZckxAGaRcemuD4fU+yIsQcWThXSxDfD2J2ury7TEAnnSh0CcgWopUx5yu5uVhDteNx3XVdtethds7WZJFI0b8hAKOuzZ0+ef3T+aPVf/L1/8PnL1//aH/+TaRyY8Jf+3X+zW5//f3/l7/zWP/3VdF42m6vvfPtf7R6+/c577z5698nDfve1r206uvj05aeMiJaJegX3MqPD/d1+1XWn59uYOl1Y4VpDWANS0166Wh9ubh/2l08eP332DLh/2M2r83XXb4C72A+H/R3mfexWUHX/cEcMrdrQRUQsFcdO+iFRcGrVEpIH8UAhpBTHYSBZv36Tcz0M/YDIquXy5PTqbFSdyH0MAUQtEsD46ecvXt/sD4f7fMxY2BpICKth24/Dpk9Vy8H3DpVBOuGpZfNImNSOyzUWkNVBkQEgCZVg3FibCiChtKBMDUECOQByEEfwWdEZkNwcEb05NmvqzQLg8gVo5hVxWenqopIgYiJ2L2YOgO5opvOsIk1IXBou4FdmVUckMxUDfv7NlYixGDMyUssmb4F05M5m4LWlBONpSD0x/2EMVCz0Yq7swXGOncQhdAP3PQASNK+6Lzk7xI77ro/DsE6pSx13vRi6Q0CUuT0AF2ZgMuYiHTFRYFpveXuq/SpwIhTDTsfTrlulPkEcNPXk7tUqQgKJbmRYDWcXjF3XdWG9Xvd9N6zXYSAIPusRQdf9sBLpZUzcBVzQFt6aM4DXNlsh6hGoldI0T9POzOf9fDweDZuDrk8u3/v6L54++ejL2+Nv/f7vnz17crY9+ee/8Tufv7z/j/+T/+Ojq7O/9v/4T3//Rz96c3P96Gr7lY8+/P3vfczuX3n+wcn51Wa7HVYJvOph72Vab/rmlPeHw+G264c+9SGEs5PzpkiCq81A43a2Pnb9NNWDzu995Z2nV09qa/d397X64e5+t7vBGI7TPErHEJmShIiEQ9enKECxWIs9taYOyMFMDap1KUladetx3GxSXMewHtbD+nyraq3NJyebR4+vGrTiFd1D5O3pO+MHXwE9/IO//3f/wW/+s12bhtBRRXDqxrTquiGs16u0Hs66sMpQHWtxO1pBQAJxoAgSCHtigboKsOojI5uhAavyVONsKgFDFCZ2MiQCa9JCcDFvCOiIzUDd3BEMhZCFFkc0GiMysQZmABChKJGpmDthI29kwR3Uiwj2nUgEQHVXQn1LnqimzkJcRRILMyFGHIYhH5sCEDggkKB0yB0AWKAOCYTC23hptWHkyYAaU9QUSIjFvUF1Eit8PLQ6H3uZ8jTDtsbYUxBF48h9P4Rp6oZV73dVKii4sgiFbYB1i1Hi0FGw0HdlDqVNsSewwaGF7sCiBqDVTIqZaNJcpiYl9IlZOWgaeEw9AxqHYvOK+8SgOCudmTpRixDcW51zKRnUAtHAvTYEqDcPd0JMNETiwnMre6XVxdnl0/d/arz6cN/kO9/5jrXy6PHjcnh4c//y29/+4b/zP/wfVbfv/MF3IvGXn3/5xWef/M//Z//+13/6j3/yxae3tw+b83MOJBD7FO9N63RExD5y47DZnKxWAzcL4ybXKaUQxlHGM5BtYmG0Qruvf/jV+e72e7//By+//ORh/7Lmkh8yEXdj32+7D7/yzcdP3lM0TJGbYII4jLS7N2xgULUEYRIxqADkajRYCCFS7602dgfaDqvu3bDb9SFyrntE7ONKrZhZHBLF8PL7H3/ve996fLb66sVXvrjZ/fD2+0F83a2CUAjK3kVGjv3exqOZcGOV5oRAkQAAalHvKMWIFJtakLfeLCAMgTpnBSMgDgGM3dVtwSQjLIZddAcFBCLxgACADGCABhSXqm4wM1ADIAkGSEKmim0JXs61ljKztTWLF3ImJPOGDq6hzJrzXphDiExkjiBE3RhqgVqUmBmcQ9MOKCohokOgruuquhfjTqTwHLxBC7xMe4RkwakaA0I57g/z7apfrcb1NO36TXCNDgxeGTFE6PvxWJL6PkYGDeSCyBBoHHpzSEPXdfFAU4cr5tGtNrUgHGRhMHa5NoeZI1MAIqbEMUVhCSwBydwRIQqyBjTlQA61uam5Itdca5tQYc65Ea2HcbZjKaW5WcUUumP11WqV4vD0yfuXzz/s1pcOsLu9+fTzF+dnj9578uTX/8mvPewfwtD9k3/yjyvy6Xazm+//7J/7C/+L/+W//xu/+s+2m0OZ7n/45WfPnj2FBhg0IJEQhs4KS1f7IYBxPhxTWhMIC4UOvYtpvGLqquXDoTx/+u799Wf/8L/5u69fHKx0x8J54rmqKK7SUV7effHpq3e/8vybP/X1x+l53gwCXg45BqxMx3k2rS4coQubs2OeOYSA3GGwpqVOYAqYuq4bVtwPZ63ZPJWUUlVtTeMYwir57t7nh69//aPh4vwHL14dDoeYxEtLPAQhAgiRQ0QnjEJVJfCQtc3gQkhkqBUc1JMBmSJadGhgDsiGbdGykAP6QsAiUEDCBbcAQGYA4ETCC/IWFLgxGzYHY6IFo0XVccmwSWyMZMXNnMBznVqrSyon1xIDIgGAMbtaJiCrqgWEiEQW6CK85SIlrVUIGiIQFREOgaI4QWEZKKAbkbhwoNCITDhFGsEUEZRAPDS1rKrVD3fHmze7rovdGsO+H4euVWDGnJuQxkgsIXofiRADGjMKBe9DpIip600JrKgFpGDe0DQuDWBW5jBZQW6iFKIHZIwuwQIBUmUewByDKgshj5i2MpSyaxS5giu1Orf5mDCsx40L7HdT81pzdvfaMrAMw4pjuLo8G7rz1dn5+vTseCj1+JDzdPX86fXr27vbg5ojprPL1fIQuj3c/fJf/Lf/g7/6V59eXP2f/0//F0W5P079OEQUdtCire2GEMxTN8rxuEclBDah1ckZqR2nVycXG16tW1UtFPpO5/bbv/ZbN9f1s5ft2I6ZSPgkjJvjbv/i4biW9YX559/7cb1/0F/wi3eeHx7y8eHOoRqnkCyE0Fo7Vu2HNAg3nYH8OO8Rue975MQxKIKbEZC7j5uulAJu3dhfPX0u2xModT/d3O8e/sX3fv/L69uw3jw+e35/86bMu344DSFwoNQxAMRA2cgXAgrWxAERzKurWWsgIoyCIoQemGejClabEi5ulOCAxM188RIBOIKYKRGQRzcwN5YlKteQlQC1NkAkCmZgAMLKYEwMbFgVDHOuTQFIwMmat8pOxoJLLd7tyOTkQQArc0QOgujQWCEEnmVGIFAHoxil62LqqnvNdYKIxH2MRjJJcMbYh8So5hIiASgAaptbq6gBiu5v8mG72z2kvi8iE5iagYEXbUgWhAm6BIIekSMABeFAvBqYIucyd10sjQAoz87sBOoNGEIDk8jayN1EkDqH0AiR3SGAsgOpU5YAwJSQwU0CqaKZVasIICJaNOeMTjEJNXA1AscQEWQYBgJDxG47zJM9fTaWvI9ROLR9PmxkVVqdDoV8QCCkiYDB5W//zb/xf/hf/4e/8As/f3n1+Hd/91s/8+G7aRwiESnG2KH5o6sT4XB3v+u7TZ6PEvr1ydaXLO0xiHVKscaqx+Mq9d/6znfeXB9/8OPp47sHOe0uT6+2Z2ctP6yfnoLL7fX0rR/9+OvnZ9uT9e9977OvKa6GUWJQoGXXR0SkqgQi0gWZs5VqxQ+rYWRcEoomRIBeazHT+WiAzn2/2Wz61blz+MkPvv2PfvvXv/PFj6fd7TqNqdsKAVi+zw+IzoyEKojG0KeoZtlmRkfgVikQBtwgF9AiEBARSVPohdyRE7Zc5lpKswroXlShovBbXa+hNjQFN4zBURDdUsAYF4kHmVVwNUNwBIcgLKRChFbMjdhRCYBMgdHNwJzNG2IDZbMl7OPERgwSZCAhYgMXVYIl/IkKiNoCEPYjdQNLxGa4gNPRTZhijwAYIqM1ABFEFmbHXEppBau3ihSY3KZd290dVpsHBx26sepc2twQzCywJBxDc2MEFMQel/QIMzEjEQAA5mZgvmAsDFsDZyHo0M3BWKkXSkTBrSWBSMGrFyEjh0WU3NBdaMRV0ijVojM0NWJlNFCoVUtrLYcYuduiuYiU6f7y8oICJtLHjx8DhUR0tl2dnz367sef/vKf+qV33/vgR59+dn93Q3wm0s31MAzjZz/87v/qP/rff/Doye7h5vb2dcnvbbcnHMSZkMp2c2mGk+1jSqB22O0uLh8LdEYk5IpAqzWSiHq/GvPr+fNPvvy13/mDl0f7Y3/+T33ta197cvK1v/63/s5/++t/r03Hf+NP/Zlv/sw3ds8fffw73+4y/dH1o+NeU6dMoLlonqMERwtdH72561xUuNdahRJarLUJ5o4SOKpZFPQYrHjgiAnTOMjqbH5z+09/87/9nR9+O6y6Z+dXx8oTOHPcbi50V/eHNyirOg9TCCA1MHShq1NmjD2SAKpVYCWSBClCICIiQtdhGKQPtUeKp+u57ebjlA/zXEwbuwMTObgZIXUcVd0gpMhMLbIHhsDCpKUpQywztubkLEgIeeFUMUoSIgonFo6HTOwgzaEiGXFTnRHEWwRIxBJjkGE4iWlyqK5kS6GSRKQzK4hIgSRR6oHYRYWdhFy1gHMXR2SwCLWQmjMYUsNmuVVt5pXmWjB46tBrOu79/n6HTmCOpNNxMrCKExpETiJu1WqDEJJqdU9eexIVUsLJFrd9gMC9BEKBNilLdEWGjKKROYZEwgoBKlmrwswEEoOJmjqoA3mb6xhYkFmcnWsLzs3Mai2aZwA/3pfYQTckZltvBvMGyF98+mU82QI+C9Jfnpz9sZ/+2V//9X/58ssXH3z1g89efHI8fvfN7RtNQbWdn2z+yM+8H3D+0U9+/L2Pf0C1bbvtdKz1DLadTfssnOba1qsg5KK17/u51IsU4nrd7u43V09g1alWz94MP/v81Xc+/uJHL3Zf+9d+ruX6F3/533lz3f7gBy+u30zzcf8rf+Nv/dv3f/oX/vgvfuNP/LEf/+7vfjSvPvjqR8Og+/1LhQkAiASwCdA0VyAYx0FVAS2lgSiomZhZ0xiTtaVRCDGFFELq+jicwXr16sffm+bdk5P1wU1ON/U2MzmQqfTq613L1ffFW3ARrIgtSkghSovo1kcvTqXtIg49b3qRXiAEQUFmXKcuJwfp1uuQtd1Ph/v7/fG4z2VfcxPiwEE4Rgmo4pxiQmGLQcEnhsxEjF6AIbA3IGKExsBuhRMH6ZYgiHQhAqk3RRBoIhQEQ8BcMiCCxxgT48A//UtXIkicmAKA1dJqhVqaqbozcRiHfrUKJBW8ETr3SEiEq9AlZgD3AJHeer9MW52m0mqrxoedmcp2s5VEwJ66PkiwVkCtTFOrs0FuqCwSlxlVA+HQ9WthYkFCBqBaHlQbE7hZJEuRIsZlQGYepjoXtC6FPo4hdIyCENwgEMWeSRrI7J6F4iibkTo2b9maG5gfc6mq3iznrNb6cTuOG3cypNT3RFJzIaKK9ZMffV+bY3+Ry/TB1Xlt8F//8197/Ojs6up87FbuJXb+/rvPfvEXfu6X/nt/OlD4V7/73W9/6/efP3n0b/2ZP361XW/GMbBYPk556lIcuiSu+7ubEOT04oLGVRjGeX9I4wbiGh1Ttzq8vvvut7/1N//hb37laz93dZb+3n/zj/7v/6+/89f/9q98/PHvaJNpv7+/v66ljttxfXYa0+O51G984xt2bB3MWu9VG1izmvPxwAj92Ke+O857JhHhXGeDGdwcGRBYiEhMEUiRdHj0Tnj6oaVQ7m7f7N883L58eNiN43oc+uQoYNKjR2uYQbzvYkrAqMQWOFTVGQqT9zEAaSRbxYGJuo42fRz70A2cJEoMkqLEMUoKgWMfN8M2daOIuKGpB4xdTEOM47Aeh2E1pNWqG8fIwUkgCBEQQHYzNSklB8YQBKAuSGmhIBDQMIWOSAgRvIaUQzL8Q4pE6joiQQiSOmJSNOMQibrpgLCQLSLnuQEIM7MYLCQ5IkAWIY5CihIFORQHwebmanm2qTVqjb0pOrnZcd4Nm8eb1arHyAUUzaJVxKytA9mE1EyNUVBmPTarI3VIytQIK5gTapCCsBRimA3BlTi1UhCdsJMWg/cxjCxoBEDRiwUhQXPBwIJQvU2lzcVDpwaM3sI8T1bmkk0YY2CktbM29NVmReARhdFc+OHmzdn5+tHp+Sc/+sRjP/a99Kv/8V/+Mw3rb/zWb3/1pz96/tX31pcnXaTT7UmIw/d/dP3bv/nt3/uX//TZ06f/7l/+759vx+m4a/X8+vpO50OKNHTcrDYATik49qEPlFwNmRuEftzobDDN+9e3/+xffOvHX9588xc3td7k4/1vf+u3UzeebNa7N2/+wl/6K//Jf/wf/e/+t/+b733749PV083F2cPNm9f3D0/G4fbzl4f7T+phKjZBlEicuqHaVOv6bHOZ87w/3AEYkQADixARMzMz1VbVxtSHzQrSmsDGcewqbNKqe9QVsxb4XuPIXTGv6MfaFO/NKkGPfCRnAWd0a94nGmLovGeKAGC6GOOyEKa+72IgDC49W7QaaqVgfe1Quj50/Thu8n6GUgBrjCEKBuF+EA4zsDNuMZGQTodi1gpW9IbGx0MFh9hhKcdhxZ0E8JDB3WAYT2OMzfbFXkSYmx9FyuxggH23ndVEREQsEDOJaKxjNx12QMzWhBIzh2QsZiYUln9Wcwd16yQwgFCnnFtzhdKsAVYkQky1ICGQ1Hnan23Wl5fnrTUmIXEXz6W5AnpgEINja8WdgcE8GxzJo2vEaCCzBEZJRKBIYOZO1lzdiUJr5oaCFELsYydiVhtR1xCILXSC5GiFyZUalVLr8UHBm0eN0mqbj+TMwq5Wa46YOGQ1ZwlNcyvmzdza8eZh82z19GL4/A9+dzYaLy5/5qf/6H/wV/7c8/PNv/jWtx7MpOu0wiff/2Se5xcvb0Ll//B/+pd+8Rd/sY/r6fbNq+vXq3W8ON04mAipauBY8qFLcei6BgZWaV+H1QaGnqwR4f0X1z/4/OWv/vbvnZ+f91149eIhmFycnmjoQFLO+29848N//V//xV/+t/7cr/x//t9/8k+YqeZit7v9Tz1//HANU5nn++u5HuNqdTRg2Q+znkGcZZdzno971XayOQuUjsc7Al+vT0Pc9DFIJUy9u7k3ML27vwlTPh/Haz3e316vHdebs53Nh6mIhL6LpQlhM2xM/eIODeJjcEaOsUMKgrNgp6rEsxm4mDHGxFEGkHUCdseaU1HJZogZAFKILR5Q64JsIMDURY4WogBWIkdKaIeQSilRDVv1WqwUdzAKzkxg2vVEHlyVYDw/fSQEpawLRgrHZrdTuQfPtRRJsN5sBdG60KXQMfOc29h3dYOqh2muCIEZiZRJCNGtMiOK1AZEC/tfEDlGaa2VeiAiEpYEWhVQObC3dnZ2sj1Zj2NXq1qzGKl4IVQhFEdBdonqzQ1EyL2oNiBq2tgrCwbprFWHHCRqMTNBZDd/e6lCZ2iRJUrqWJuCozAFIuo7VpjAqjVHIow0zfnQvPfYi6TmCFwBDvup1bxabUiZCKFps8aCTe2wPwrjYSo3+fCNjz5a91Ga13z/m//i164u3vvZb15++N4vXV8/XN/tdruJz7bjENdjf7IdTrfjy+vdw+64Xa/IJJdjLsiAh4fdZhxaK6rq7I4IKU3TNPa9AlLOOO2no928ub++PR41EUFTPTl9FPoVCneMrDXI9td/43c++eKlGDx+9rhym49ZNAg5xuN773+Y7+/qsY64vrt/YKJN3x+Ox8P85eZ+d35+KtwhaMlNbV/g2HUDOakqpTSElThAdtKs867cvhzPuv0DhJ1dpnSAnPXutswHqxj6kTjMURM2n6iexN5CX8vBRk1ElEIima29pUaYUxBomkvuyyp1YwrcRWBjDk0kOx0n74BYSuYmTubuqG0idGZgQRZRy0DGiFWNAZlmwrddWVOotYJ76giwAmoXV31cE6yT9ICz1sbUM+t6cxkOCewNtGkuX55sVoKkHHoJSUjMGnYJN5GxvL6lXa3IFqIQIyISizZD8iAjYYoSY4zFAEFTh4qk1rQWiawdxRgL6bAZ3n3/nX4YJDIAKDGzBcekkaJKQGJkB3ewBXGF2lqhyE0za0NGREYHAGJUZ3BVwkAIhNIlmetsFVnAQR2WZg0G7piGFMHM3QpKd/twmMtxDKuRQ1QQQTSKQ0fN3X01jpyiOSsGQXGstbQgnLowz3MkbIf9x9/7+Bvf/LlHXZ8C/+TLN5/96HvH2/NHZxcXq/Du5eWwGnPG41wY7Xh88/rFDj2ddOGwv/Y2TfsK21ga1Fl5ZDO7u7ndPr1crU9mYNWctUlpqyjl/ub+Tg9l7kJ8cv7ouy9/cv/w5qNnT997/8n3P/lx2be+96vHl7/7r37rj/3Cz7///N2f+9lvxAjoftjni9W6f9Thm7N3nn8YYzvcPUxVI8G8oHvQicB9sZBAbkdCjl0ijs1M3LRqiuzgXgsocC1nXboOfZ/Oxow3fnzY38/+gOG0A3ZqHcvs6cErRwmcu7jp+zOnvdGMXkNqKAahq5MyepcSczOdSdFhwI6jIFOHMky1Eh4DMHJH00ywSLyQoBE3UI4xsjhxZQDX5mbA5JUJWMgDh0M1V0c3VQPEEEXtSMHGdO4tND0mEcS+lTnISJgRXQjHYZhnPBzu+Y/+2cvUdyl1SOIeI28IxLQyoVmLkVab2A2Eoo7NqQmtwGPgbkijA2TNZiDoISYzq9WJkQjU0A2fvXN5dfmo67oQOkIWYRZo1cEpJuLAwNS8uDUEVLVcCkBl6hyMpHFogEVtViuIwszL6hAxikRhNjd1Ww2pD91CDGKKwn3gVR88BGyoqkbZo3ehha5QLc2ysqNqy63lVmPfdeM6DinEnlgChSBSW5UQWaKWlvq4WvX7w8NqPRynPHTdo9MNWUOk1ijPRYKvTk+fvvvVbrW52x3LcRo7mfP+9asXh+PdkydX8zHfvHmz3Wwiy6oPrcxuuN6cxq6f7m/meV6vNmmTdvv9pz/40jltt2fNyz/+R/98WJ1/5WvnfaTjg315/ZNprjUj2OH8LH7l6187v3x6fn51c3370xfnv/DzP7N999ynGeaJo1OKrRqWYz+sQ2RwB/Ba5pprSBIiSQwprJgiCxoAA6aU6HTNp5d1nsvnP5wPD8WpeGmWF+yxCG/WZykxkxNDETW0fsXrvo980qXTfjVwJHADrsyBObh6yZWpmU9BJHapG9dhXFHs+77vuk4EAYpCDUHRGREICd2YM0J1N8bRUTEcJahbdXN0siYtmyl6k+mo4NQPIfYusaaUJCiHJsIIIUgyQ2SIgYmZWNUfgBaRnpQyi7sBLLXXGAIzC2MgOO+HzThMc74LPHUBAHH2FiUwbaCFwEIk6gYACAHBu745Q62iqpDquG3b9dnl2aMuJuZKqJJ680kVmlUKGGPX0JAYNbtPDOTQSrsn2LR0tOp6UOTKoZhngAYu2gAhIjCQInit2KoyOgMKYzMjtsjCQIgG1DM4uNRaI8fIa4Ku53i6HTonyVrjnLX0ZlqbKaVhQMeaW1EFzAt3T13TagClebKu1/v9w+bk0hQS89kqhlV4+s57DolD7Ffb1G3vf/RxPtwDHFXL4eF6f//iGz/1zXUKP/7ki5PNKiZxqNN+tpJbSLkULCUQzvv9w93r0Dk18wIGPlyt/gd/4c8+3F//53//n/7Wevtzf/wbf+bPBwzTTz59oV6/8sHPf+3rH55fXqxPn96+ul9n+qO/8JXVkwHS1jbXRY69REtRYgzbUd33hyISD8e567qzs+2w2ixiTwldikyRCsAqRsLgnsBKvb4pu51wf77ZuCrNxzXTPGwPWqd8nKEcA2byah7HFIdukEsSTCnEOK5W67t0d7N7bZ7BaEwJSpunElOVKMWQKFpLLYRZ1UU5hkS9ouXDsU/CRClSiaHOIXtDNPRbRzfbgwKTAk4KRMwUWkw8i8WEhB0zEhUWIGwE0bSW+rILjPRIiIikqTmZBGaTaMkFo/RdctFF2GgogWJMVoBAaHUWq/ehVV05v0E+IlFAAmdioXjKHJrtQgjAkHN2qk1nFusHKgVULQVe93FkitGRKpCR/P+J+pNf67Ysuw+b1Vpr732KW33Vq6POyIhMFsmsWShFZoqkIMo9WZBNwLAJgXDL/4XbNtxxAf8LdscQJEiwZcqURdpkphTMYGZEZMQrvvfeV93qnLP3XmvNwo0TCZ/2vY2Ds4s5xxpj/JyITg+H5qcUHLBlApZBbVE9NlsNloC2tjXrCZFrNVp6cjUzM0U3VRSBJGzhxEHuhJDOk7tXCAMgQD/30GcmkamQhaYhcM83OW990ZwMA1qbQYiBhzx4dkUHSiwkqZjK3e3RasWwy4vtNMrDQwUexu3+1eu7ucdH739Q5yaMdGpvPv30+snzu8f5MB82w9h751hlHE+Hw2mZr28udV3eLrNgJzZdTwHQ10VEWu+n47HVU84Z0L0tbz99ef/u2Gfc5k2b35HO//Qf/8PvfHj1X/7zP//xj/jjbz359//Rf/Dys89B6vX+vZtnH6Vk//bHP8F38+//yrcvv1O23/jYAfrpNHDMdZ0fH4ZMabp5nE+YIIi16ru7RxnGcXfBNJrO3TopZuGSCAS9DKnsY1nScm9k0TzaPGplSVXKgx57j7maCCURDZuwW8FSdlOZJHOmgbCMCaa8B0rv3n4GAMBaStZ1XVsH4nErqgLGBALAramIQ9AgyURQOkCUlIvgwbq2I+B6Bn96Rw9IbIDuWIlJspl5KjQMaQWO6H5O4jB5aIR2XcEPwpKS9NqBGlhb7QG55iFcM9A0JBIian2dvLuxh6Q0BQ0dA/qc88QpB+W5vnW8x9GEx4Rb9NGQNDZIRuzd527NWpAMqYTHkShNw3YUFDpiYaJUa3cEj3aY78zbZnqfcCSxlFhNmrqBUiCCeDR3z5k9oveOxBFcWycDpDDrkhIEIkjOOLpiMAmoVnAQzGmgUgawwhTCTE5ND2s/rP1+8H2OXI7Jumbivqxnbk0eBhAGVQFW0IfDo4dMmyvQ5fB4enjbq532+10+oRvcvb1H5Zur69evv9Kli+i02bemBHUYhu3+KXRTa7VWLsPS1z/74hcc+PzFs+69zUchppRJ0pDL4eE+ZewGvbdUm8D4+aefIuL19UBwXHrVQ/y93/31j59vfvwX97/44naVtttdmK6P90c9vT7V243rb/zgGz/8G9/+xm/+AOf7u7sv6esvl9v7h9P9Os+cmSWXzK0fTWMYpnEc6zq/efP1MGymTWKDDuQBO/dcIF1exm6rL19ZXa3Z43yKtghALjv0erCRw2TcN+kWzYFHzh1DoUqBQXajjEFhgJvhQrj0eX3z7rOQYEwi1B21mS71VNdiJgREQF5BGRzDOvIadEQU92IagmnIu3kl9zVgtVCPCO/hCMgBkThBDp9SKCMtjpVQzUzdkEBjtR49gKj2BwDsm00BaA6Hksc8khOCIzjzX/07zxF9KCNzWttc0nYqF70fgHFbLnMpw7hhzj26S2zyLuX3iEsuLDJYgNq9xXwGPSI6kxBDRBdKJZdUNAu5Ta0v5qfj8TQvBxKcNluRDMAiBFi7zmYdSM0oPInIMGbHCgCIatbMDQLOldZMmJMQsrsHNGIsXIiZEqTETIWgRISTAWu347G+e2hvVju0FU0TIhbJiRFA66qnde5avVuvvWpraw0FEnFU1QUcm/VaKxHnknORaZiOp3mcxmfvffB4Wsf9jvJQNnsQceBucZoXX+NY5+7dm6w93Pokw+V0NY2bzX6nIM2RWXLiCIC29FM9PhxYUvda58dNIUmkKV1urnZPrs2W5/v8/Y+fj8ssa4MKV0k21F9M4298/1u/9lt/5eMffBOr6e0Xh5c/Obz69PbVq9M8e2KAYTtuKW3OfZsEhYWW9cREu+3IWEjybtrQIDyOm+tneHFR1fL8iNHBJQDUZgZEigqo5AbgLJEF0ASR0uSUej8A8tPrb+Zcgky4bMap5N2YN65a68ndiDqGumkZsExbLrs8lgAncEQ1gLAea7N+7GvvnVRb9Eg0ZB6EKVBrnU0Pw6DMHSIwJkQQHpgKIji2iECGABNpgMmU3N0dIdgciCTxwIKAJFRypiR4xg7LmYnRfUk0mLfH0xcMw5Cv1WoETuNAmbhcwtqPMxDtmCggIQYlBLRlVcbIjA1nd4YwYmVRpA4MxMnAar9b28pMa5/XtqR8QbTkNNQG7iSyT+m+2S06liKIjVMzBwKOAA9b1Xp39jijQ5hwmgpTaq25i0gaRvaoa1vCA9GQmHzPUCx6gBKhEDHkkrc5cq0tyEg9WXSdwTHJYGtvvaoHEVade7dqKuQXm4vLy+eJnhzuD+8ejiWhta9fPHn/iy8+Ox6P7733wZdffjlt03vvvzBt93dvez3tx+n4eISo6zKHb3bTNvMul01OOxl3GuR9ZQizCGFUe3N/qPMpDHDgVHTaDbUu13SxuxjX5fZyfO+bv/17n//oX7dXX/2Nv/5BryJl7xSU09X1bn91CUyHd5+9/ezTx1efx2lG9WAvJVPZiaSu2q0iYs5ZhnR7+8ZCAz3labsZVdsay3W5mvbX+PxD3O759dfWXVCggBpX4PMvlJG2KTFlWE+MVPL22I/OeMWbR5Q3p/a4PDy7fB+VJTkXZh8vNs9/9Zu728dvff7lHx9PD5mFSle7037n9myda8oDlKzeCQJQgCbzpfVTEhQeG3U3hCiFc+KcYnOcX2l/ZAmkRMwQ5OC5hBlsfSglqfFadZ1jt0uqDREJu8ZKPGKEquaSQ1vXNWlGEUIgIf7NP/owQJE6IwOw9pN7bKcnpiCplIGI9WwvPHNJI840XgjsSK3pg/kM5GrN3QMU/1K2LDkTJ3BHwtbWtd0i9zNEdbPZ5TwAooMKJ6Ta4y6gIhkQMmfhjE4WbgRq1lYLdURiKkycsginlBKBIKBIC6qBHbERG3M5GwqJFPBU28GashfpucQ05Q2rZ2WzkMzQuq5rCw03MH94uJuPd64KjmOZovdhyMRk51QfATGuXbXZl19/zmLf/va35sNpmQ9FcDPIpgyIQDkzpm5wXJaU4vrm8umLD65u3lOWrsHImSSlTcrldLrvyxyhTuAEBKXwkJnM9Ob5c8oC2vLTp1ff+A6YOegK1tubm+vt5eXWfK6nuy/+4s/ffvazh9df6O1biKajVIoIYLXT8e6oMzOWgQWTmQJaGQphRvCcMyQd8rjfPsnP3mtPngWwHA60Hry2utbQihEQFGEBMUoWBAh0pIjgcMEMMmHIqR0Wf9hdXCTZEKWccs7E0VzzmLeb6RpCgR6YkaVDAhBJSaQUR2PqBNDV3KzVAMVommkETdrRHUKVGYVFqGBkt2i6AqAbeTQ9m9uchmFgQmZmHCIwAsw9gokkfgnbVUKJMPOVOeU0MiVm5t/9ux8gAGNA9MSJWXpfAWPIl0Rk4QFK5Ka16erYVR0QIUXzo8JscTpz7AhSnIsmUMwBqeeU3UFESr4UFkBlSgERUHMap7IFyGfOE3EAhvkxIBCT8MRU1Fuge2Dvqq2nRCycU2YmRCRkIXE7v+AWEhcpAApoRAU8hQOAqtXojpa2+eqqPB/lisLPAQcGtN57X60vYG2uy+FxbrWatd76kKbedZmX+eFdPc1tXZfl9PB4e3yc3729q7qC8DiM3/zGd55d7w6HV7vd5vmLpxDw7nDgnA7Hdqo+bbcpDbuLp5c3H2ba7i/3uWTATGnMY3G2c41MXR8RJA03BLAe35YMyLodpuFy9/j2HS/zw+tPJ+oXacxEaPU4P9y+/bre3375i5+vx0cm2G53eXu9WJvvXq+HR2baDFNAAZSUSkrD4+Hx3d1d019WWbG2unYUfLJ7Nl4/h5sLzJLWNR7eRj1SUO+9Loe6LNobgKZUetha1yBE5oCIIHWojDUhgHSoBCeSQNrsaTcyS55EBrPWVYlxGIZAwKRgwZ6GssWcEIGD1ZJreAuKAqZ1PtTjgl7AN25I3ImMqTBtGMC9BXhXRTT3CE9MEwt5dAqnSKEJkdW8N0c8X+iZQJjFzIkj8EgUKWWEMUL4N//uMwQkCsejx0rE7r6ulTkzo3lngSzR+rL02a1rt+ZmUR1q99nBcppMAciFCwITZCICNEASnlKWIluRaRg2KY3qTeNAojlNzImIkZ2JHFxtIaKAQOezP8kgCEHOjW0ESCgUQkKQwgNC3IhFOHUWAECECHCE5JbDE6KF1mWpvdWESEb1tBJohAeAu2lb6nqal1lbm+d1XWpr7XhYPKSZHU8nyJKTLLU9Hk/BnMdpqXpaWhmGi8sXV9dPW4dpM43bSUOakYzbPO7m2jXi9nAyTfvN7s2bl69efnb36heobZxyLoOUgYSBE+btar6uTahPWQJsWe4BYLe/Spym3Z6vbyhPcffw6md/dnv76mKQtq6PD7eZqLeVkJ7cPCH04+H+1Zsv7+9uhWjcXAgnN3dzJM1l0yrmzNuLATAlkSyQKaWRd8Pm8tkH+clTwIDlEU4P2Fdw8+6E6FbrWgGdIlQVgEcURLQwDiyQwKxix5QLJwS25bTWW0g6lc3Ie85DuEBg01PrRxYsU+kRGIrgwjmkgJNWA8cwjW5uBtDD6vF47Kp0RoepSR44iTC15QCmSKJugBqRABIBiXBJjMjaomu3sF92iSKJnBOPiTD90stGzX5J4B0CkX/n77+g1Ck1AA+AAPXwMGpdhyQoDtTBWrelWTPV3mvTRqIAZtbDgs48GDDhsfCQsCQuCIVomsZdwuRuEEWkcIZAq3111yFPeUiIfiZAqlfVE1LzcGaKCHV0Q4ZwdXRGKYSUBYY8ISR3AyCiAgjEHUnPvUoe6sFho5mbG6Cfb1lXTLDZlCvIyXu33ilCl7nNKzhY6+tSAwGBSpnSMALS7mK7ubyYu5pw3uy5jIBMubAUyeXi+ua9D9//zne+Z8goG0rTzz/7sne4vHl2ef2+Y9w+Hj//xZvPPvsp6u0+WfS3b7/+8u3Ll4e3Lx/ffXH/5pW1Dq7dMlAuicaEw0CUZRqvciHiVfJlubhyskH9/u27k/WqepiX7WafZFSDQDo+Pn76sz9f1+PF9uLm6nLY3gjncRyCKMBMDcJLEozedWWAUpIwMxZOuN9fjlfXeHlj6mk+eDthCDms6+peEdzM3Txcc5GCTIBLdAQvQYEAhAIwNGPjMGx4bH6n1qRsUQZwNYvaZrXwqMgn5CBCD2+xIlPEGJGoKzb1bm5eewR091n7sta56UKCFAgYxDPAQXuvFRTcaQ0lwgQYgMqcxrwJR1NTg67GUphHpjQOG6JMkIkSoDFzhHp0dwcEQuff/QfPSZokiyCIAVB7r2CpK3poKYwcblatdl0jrFvvtgauET1cAwOdEwlzZpAiG+HkCgRjkimn4upmvaQpJdFYaz8FNWJmnCAg2LWDmgY0jx5eCRORkAiGgBPTGWWQkEkwhCGXQli6dQSASAiB0Ym6hZ8TvxECPoQn995bVVPVlaJM6TmRjDGOeaLmqJ44BcJpXtBjd30pMqSc8jgCwDQO6u14OAZRFg7wpbXjunqzpzfP3/vw/Wfvv/fh+9+cttvtfnNxdXV98/yb3/w2Coz7KwVaWisyHBd9/frt8d3x4dW8mcZpzIe7tw+vv26n25wd0Y73X769/YqH/X6/l1BAuLjaPbm54pKmnJx92OzT/mZ2OOcqT/O83T15/uELhAD3+4dbh7bdjNvxWpgpBEW6LbXOyJlTRsDWWtXawxBkGAoCWzMmvN5fbPf7cbvjy8tIA5vButq8mHYybctpWRdhTokwFJm8K4UVIkJofQbwRGmEYUojYnaBR59D5qiPXcn6UJdja21tNaB1Xd2XgGqmoBYBYG5OLAUjmi6GEd6B8HR81DaDN7W16WPtD4ANaA5YEdW9mYV2NT8yUAAxMhOnPJyb5REQAs9RKkYch2GcJmYJDwACQGZ07wGru0UEc+a//Q9eAJowsiCEuFNAj/OFhZESpETmuKwHgAZotaNGM6hEjhAQmLAkzgQkLICZiA3duqObIJ0nImJImQ1wbQfJJ4DeFVgKAES4+mLekJxQEDhQRAT8zKqJwCBEIRA2YkcApNR7M+toyV0RjOWMdA13j6CwgZHN1LyqncJUYJNwlyTllOGMrWQmFDAbE1rCx3mta3N0RwOI1joylmkah10gzGvtapvtzebiPRq273/8wQ9+9Qfb8WJZ5ruHh25wOq3N/ebF+9Pmcnd1ldJwOV2msXz2xdePb05//Mc/Xh4fnz3fmS1lmvJmd1zx6y8fa52PFafL6z4fn+w2u93GrHI4MTCmzfYJbS/o2VN69mJ89lESsXle++0XX3x6/+51Sv36YqtHhR7Nq9HWBQVjmK7S9nJ7ebnZP91fPxs3V2ncbC5v9tfPAXJrzaypLZToaroc8uBWQVLwQOuih69769ErQJRcIMx1TcgRKJxoKLsyuPW1n0YIs0AQzxIECy4dw0OdHtfeQn2t2rq2puu61Krae3hXbRjMQa33gMQ4hXfFNcxdQaNq8/UwW1sAmqQIt+bHAM/iwoSEQU4Q0FuEwS95WRkipZQDzdFYBkAA9CScUhrKQIzuGtBFRgjwaETqroDANAlKCApQY2JnUw0IQlDETDLXhkQM8UszKgAFWJha9CTZXcIzJpbCa1OClGXfdRGowaKq83rajDtAt6i1GRBP04QMSxxnu50bJt4xMxibK0l1QCIWCgeXxO5qpmHOzMzCrIBu3t0PXdWUVO7JswuAOiC21gMtSYpgjQiniFAFw0Xkdu7CcO193eCYhKz3eZ2jrynlRHyRL4ahJB4ReHc5TdvLTbnsva6GKLzb7wFk3E/DOB4ejlMKwNIJxqtrfTh++epuHDdvf/JZGsac83e+8yu765txjx9x/qu/cfvZdGNtvprg3eO8HeDmYsvp+r/9f/z3kYbv//CvfOuTH6Yd3RR976NnqdDpcXdcjuI4XL9IH/4QedRa1LBgHp9+d3j5uh4O2lgDb2912kTsbyI8a63H+eH+kCU9e77P084AIRxRym7POqmqOqXt1YYQakFInGgtMl3tMA3YV9CTrktTXGuV0JJSBHiQiCAYNgoPDp/N3X3Hg0Iw+KJ3vUonJGisliWbpNnvji4Z6t3yNcOAXrwHi4/jkMUXe2xOSLn33tdXQAhERIKOFuLazJeImjgDSBk1YkFvFov6SpSIG6ckPlltCGBuAaC+5gyADWwB9FIYURyMkzl3a2BmyBhQIcQjEsMml9pobUf+rb9/SfJLJlkEmbmquiORMDOiAZyH9AjobtAtiNzDhAUgtwYYdBYK3QQJcs7h5NEBe11VtXev3RbHGVAlDbvpwzFfm+m6Ppr3iAiPCDBvfH7AowAgkUAQACA5ESQGIkRSQHMPU/eIVuv5n8+O0t4XDwQfEIYwMlvDvdnBYUVoYIzOBMQkjMPu8mp7sbu4+mDcPN3sttvNuMm72tbF54fH4+H+9nB8d1yX3lr3pqDH+fjVVy9fffE1qF3uNzmncZiSlK4BKM+ePE9p/Phb33jy/PnD43K7zHOzgfFXf/irtbef/umPrgeahn1d1By318++99f+9u/87X90+eTZsJFhB9///kfT5SVtr45LJ9kbXUDZysXzmJ6i7CnQbcXsknI9HTGw1SopHY8Ph9dfHW5fh0X0x77OGt69eqvH+9vHx4e6HqNX663Oj1GPbjNCnE5Hpba/GDflxgKxkLpGjxwogCA0lUxnyl84WjVTAwY3B1PzgDDtGBHmGkaqOZrh2aLcQpbAUOOIqh61Ha02AEQyQmEe3G2p1TQIu3uLM6i4m6ubrb0drDc+E9+dmItIlhSIa1A7XyFmFk6BtHYjzkSEyGoKUc2bhUKAiBAhAgqR1e69BaJZR3JOAdhTzqZW64NAsKkjCDNDIDMyh4YheWvNnRCxUFY1D3UHj66qAVhrD/cIaL1hzUIbAFzWW7VyfmMAgPDU/RQ6QzfiseSh5K3AfsjQR6yrL+saHCLOIIQjROME4QaBgAoYHt2s5pIAgZmBCMysY7fWajeV3jtg4iTaKoRQZNPgpBbNcZXEGeF4WhGn/bYQcNnuPnzy7aLT6XSICD2tx4dX92+/Ph7fHg+9DMP1+0+vrm+808PDgz0+Ho4P82nd7C+AJafNzeVFwOOsx2++/2JZV440bfYffvKMgi5unpXtcHn5DJB/+ouf1OW02e6b6nd/+MPf+jv/7p/+v/5vacn9MGce5lN/9vHTsh0fTn/BRb713vsZhKdLvL6+2F0Vhfm4Ig0IENG5XADIcvdY71/P929++tVL1COsd+8+e5U5T/tLkSFhlIubi+vcXFUboe+m4TifoEWNUIr9tAGFdV5yHva7HYtspt32clIsAqW2pdcjqIX5KGObj8s6n5sX2Ik5WzRgBIDeKyISJfCIcHY+m5IvJABdjXoDyLJLcDyHnPwcGgkAdzdtgJDdvbUmpOAQ1swMQZiL+axewxMih3lgi8BxU5i7Y6di6MA0DshGYM2xUxgj8jDuus69P1po+BgOzMFiZj2UEKP37q6cxCHQFSKpsqOyuHQTCQscAEUoBVYmArZzwRGj1Fp7u3cToGph56veDMCh95WBOWHH7nIEII9u3kQE0T0csCNgRAIkhGEsH1CQ+ZFxyGnDdKHWkZQxASIJA4rZguSIFJ4QAkEhwrpyBmI8YzQD3dzVFUnOZtbeO5IRhRkLgdpsUN0dnSPixZOP9uMnu/E6y0U/pTef32d/91jvw1MySOBlumpUPnkmT69v7g/rl794vcz3X339Zcrj93/t13/lB9/cXT0tJTHgm7evZYDvfveHmyJ/+uMfHd4enrx4f3g5jjJhypb6Ju+2F1cXl3tK47u7k7V+ub/8B//Rf8yF/vz//d+40pYueXiugD0erp+UmyfPQqQ1fPz8Dbw7lXE8mOZpt9vcYMo4XUYQAuY0PR6WfneLy/z23ZuLi83m6YeH+7d9foCgUqaH+dGss1BO44wJEZOQpLLd7NdQd5+myWa8f3gzleEb3/zh5fNPdALYFF9x8BwsbVkMN8vxEcy1r9qOIrmkCyQgWwOpmRJDEdEWliDn4oa12QzawRA0eRr8BmJV6YLe+yPBiJSR2Cz0rJifE61xq3VlZnDovZ95hKDkwIEqYhzQe0dTzpEHYZzCT5wNY0ZQRt+XKadhPURbySCm7XZ20/5otjCJRVifz0OLKTZorsGwAYpEzlQANCWMmPjX//YNITGNIoPgCMBu51lIGcgiuum8Hj28+Vx9dgcIOHdUh6ErmYEbtFjUzQPcGlCLcPMeYU3VVAnD3YYhl7Jz70jNgdZl7loJf3l0gIgQoP4Y0d1NNcz+/1xLScZ8ztqhqrlFV+u6CGVJJgkAAtAIi/BU67r015txePbso+dPP9oOVxB+9/Y0HzRmTy7bssvDkCRlya0S5OHF+98s2/LnP/3zn/zZj+/fvZxG+Tt/8O/9td/5dy6u32vt8cc/+lf/5n/4Vw9L/fDb37u8vulVP3/55VznX/2VXyUuzaw3W3tz1K56eDi0tT88PK7rcnh8rNW/fnN3cfPscP/49avbZ9/5zrf+yg9v3n+yvyzbbaHe85C16csvv37z9u3bV18dH0921FrBO3FmYCAOkpSBZZ43T8ZaW6uWtxcyXUTaImdkViSL4ODoCJjN4PHw6BZ5zNO0ddWcEwm6ei7j7ubFsL8ESBCdH9/Z4XA8nUIbWnNvlCiXyT2BOVitfZZSiggh5fRLJ3wgmWlrPShCO6IRgEOqlAAJ3Kv3WlcwgkhEBehMmuZA6FV7P5k2ACNC89XiUbW7ckDyUHfDAADo1sGBmTglQMvFU+phIbGlNBbaYmSmbOoQHCTdZrWjqgKERXWHAOy2Nl/NSI2ZUCSYIrAxI/jAf/VvvUAojIVRiIQQEF2tB2A9Z5V8qb0u+titOXQIiFAAVVPzMMeqq3rtXSHAzAwa0HleNLXQjq6E6O6mtm42V+DSe3Pr2juCFN5SMIaFgzkEHM/lvQBohqYI6IieEzJh4uLOvak7uBFCBJ2IO/NZMRUAPxzf5SKfvPfXPnj/G+Ht/uHtq7dftv54RgpMu4sypq4GQeF97X3aXl4/eX737vVPf/KTLOWjDz/4tb/+19/7+PuHU/vJj//4//mf/19+8tM//eQ7n/zO7/9+Gcrt/cuf/OTf/Ot/8aeb3f43f+s3SfI0bcowjdM2l+G9998XKRHw5s3r27tbRJ4fj/evb9+8fptSWufaerz3jY9OaxMegFwoj4yPpxOElYk3+7LfbikPaZrOX2czlrDqdY0MPE6jlCDN28u0mcZxGMfx4vLpZn89bnebq5tStl2ja1vnQxIah4EhKAcCEQoiJubNZvv02fvTxQ4ZmADqYoejnWabZ69zb6e1rlZbScO4uYAydCYklmAWdnU3CwQkBlPTSgSFIKEYRwd3ICdS4dq0tdUCTVurHXBTZAQABAojdBEKa0boQhgOEMGYzTzMNVqtzR1ZuKtaRzcSjlQkSy6pICRrpJ4RBGNEQhEJn4BSq8eqS20NEJjRjJgKIrYW3SgiGDklJjrzcY2Z+dd//5MkE2NCBBYCcIfFrdfm2gJpAHIkMa/uCuEQhGeOjWrtvbXVXNXqueoROQCt99pVza2banckR0SEFFEtOjP3Nve+QjhDKrJnZPeqHmaguro7oJ7dHBZ25hJkKYTkjubo7q5gHZjT5dVlyQOLITU3bX19//33nj//yBq/fvX5w+Fz03Z59bSkJ27MRLW21uYIAEdC2eyuUsqfffZz6/X73/urH3/r25bwZz/9ydef/uTf/A//8nA6/d7f/oN/5+/+h6UM/80/+69ffvby7bu7d2/vf/9v/cG3v/WNT7/4XITv7x+Y5OF4GIdxOa2Pjw+1rvPxdLG/vLs7hMHTp5emvWr76osvNlMp2/39qfVmjw93Tfvj8dANTvO6LEsZNpvdk3G73+y3V88+zHl0DsgilG2pvR1ctKOMm9123LslzoP2vswHlhRqZ+CKRbNePazkYdpsw+N0OgYGBITZZjturnbNqi8r+UxNI004FEksOTOi64rmbTm1eoiwcdpf7J/k4UKNCAuQqGFtBmAiAozd7NDXU1XFSMwRtCB1cK/cWlvabe0HbcC0JRAzBQtizmkSGZNswjnciXJEQPDaeq2Lanc3QiIYrRPhGKAQKLwtOTOHe1Nj6yC0cSPCEWGDnkTG1oJQzNW9MVLOGSm0IYYw8FBKKgnRIQzAkiD/8Hc+BBfEYCZJRESA1d1NEXxAdMbMwol2DkdE4yxwPnN1j7P4bgYeyAEYSKo2q60BsKxea3XvZmdIt0dg09X8/oyFJMwIwJAIsgevTdXMTQOWgBXJkQ3AIzQgMJRJACjO61SHCC455zKM41QyRaxM44fv/fWw7Wdf/Oz28c9LhimVzCXxDoUtDFhZyij7ga4SD2XImYY3r969//7TFx9+0vry2ac/ffP6K0Bb5uVb3/neH/y7fxhU/uV/98//P//yn23GPJTx8Nj+k//JP/nkOx/+V//5f/Ybv/Ebr9+92YzDaT111/k4Pz6eqta+VmTycG+aNsPd8WGd2/Hu/k/+u3/RTqcn738yPnlG2LRW5rRUS0UOx8PxcDoueqh+uF+GtCspd43e5inj+UuLG5UBmTFAhlE5aUCdT97XeXnwACDf7oahTN0ip8wUgNCr9bWu66Ogb8f95uJm+/xF3jyNMtqqUHLKA4+DDLmdHm9vb9vpWOsK1MNbNCPXrvPaV0q5RxBhKWPJA3AyZgtp0NXDzgqdW+2JbDBrZrTWfpz7Wg+99zFfFZnCkZKklMKzeWcqSaYAWrUihCOYmWqPwAAgJqGMOGh3wKZq4SJiTBAqamGWwYaEe9XkbhwSwUxbBEZw4kDknEoAIExsmTmxEKYo5SzSOLPwr/7ec/csLGdOJWAn1tY6YnZI6uy+JhoLjxAnJ2Uq6bydgpuGGZoZMYqEgwVaAADy0rur16WvazfXc4ejQGHyiBYognvhseRNdwgX7Q7B8zy3fgRcWSwQiFiCVB1RwNWNgVK4RLAjsZSRmWguYoT09OZ7Ty+++fLVZy9f/ylBSrRBH3JycAsKR2EZCm2E2fqKUZe6YAgBvf/BjZn//C/+4t3X9/v9xZAHwvFb3//19z7++N/88Y9+/Cf/QlA//uCDpnHq/J/+0/+VZP4//h/+9//j//g/+bc//plr9N7X5XQ6HPa7C9XOIiTcW3v71au5Lq+++urlp589vrt9/fWbhEgivbtZu7m+LLkA0DgOKLSZhsubJ4FlmnZJxt699brMM2UsebDaHJRKZlDozhCSCyTklKftXsB1nXdlAtMA2e33GxkQqFnTbo7LVAbJMkzDfnOxmy4pTzBNaTdpnpAHQuunY308Lod5OT4SaBJGR28W6ODd6qz1BBFgDcDVqoECMydWaOqWcGBOGp2AEw4opVU86G0zPa7W12BICFlSieCSR+GMZL2vqh0xws3VIxohmC2B4eGImGgAb2ph5hY2DsXVXJU4uquqthm0p944U0HglMYADFcwAnDiAJnNDWFgQpYEQQQpCaQUgOFuEc5/5W99GAFJikV1vGd2ADHzAEMcTfGMI2A2gG4QiMKczw1eql01EEEkSUJCwcAI0O5uoKrr0lu1cEIk5jP5DvFMNKANQgiByNYcPAzRm9VmRwtDNuJAICRB4nD0SBGICMSgrUZISVOikGyA+uT6/ZzKF1/929PyrqTLsByAAAtyDZSA0KhkuyFtMZwxt6qm8OH73xnL8Pb1yy9ffhmY3nv6otU+7PYvPnxxeDz87Cc/OS2nm+vr/c3+2KjD8I//p//kdFj+t/+7//V/+r/8pz/+0Z/Px3m72Tw83B4Oj5dXu81mmudD4vTyi5d/8q//eK1tXXqta1+rtnW/3zx57/2y2R9Ox7qcrNec0m4zmq3393eSuJSpbHb7i4vtfl+mcZimMg3unZk4EVgXp9NSva+txTrf23zoa+3C+2cfTlcf3b37st6/u717dby702UGjnBf15W8ZaKL6+tPvvnr1x99J64uQSTuX893vxjmxefj4fhwuj+0w0H7KUDX+dhrFYrEjIjMMpTtMOwosRCnRIiBEda1rrO1lQP4DAwmBweDaGhLX5Z+mNvjaTkqENHIngOwW0eglEo4uyOAR8Sy1NZakAVo8/WXnrQIIgKyCHMdwIah7HfTBYS0DhHWm2r3uiBEIh5y2gEmCHCNCHOwbtViRfRzzJ0pRxASpAzE1nUNMCbiv/F3v+WODKnbCdP9X7KNe4AhbgkHkQAwRDKvEGfjqHt4hJ/1n4Ae2BAzIqVUhMVUPVxVe+vaGSIRoUggMhATsYGqryUl4RRGSBAQgCFE7mreHDWdYw+ACATnERbwlwKUe1WNgKHkcHrx4kOi/vr2T5znBN+oc3RbkT0zu7lia14FnH1ASyUnW63g9fe+/YP5dPzFL/4tIux3N1fXz+7f3F9e3FzcXL/86qvj4TAN47TZ5GHzcFyniyd/8Af/6IvPvvw//Z//N//zf/I/++Llqz/703/73e9+e56PZjbP8831k88//6zWen9/+2c//rGp3VxePX3yZLfZJkmbcSx5SOPu4uaJpMwEp8Ph4f7BzIiCSBSI0tia9t7SmCMsZUbBcSitt2GYCISYDex+WaOfbu/eYgRpu7978/nnn5cyfuvXf0uGC0Q9Hm4PD4/Wj4wueUISCNmkzExyuRuvngnvMZSWE7QmYXpaMidwPTzcLo+PHpZTTkTuKikRkZ5FCaQIOFvIIlC1aW/g4aG1z4flsdpqqCv0g9XHtqoeO+ipLuaRuaiRGSRh844Apme8BZnFuvSlrgo1wAw6UhAKoAc2woyYhDcpFea0Gfe5TMKFMGu3up68W0DyYCR2s95N1dwbUFc/WVSRxFxK3ggLIatrLnxOnhAlQubf+3vfIhBAN58lGwsQG6GpBcZQZApvrTcPsqhBfq7Ecg/VGkHa3axCBEIOByJMzGbd3CyUQCIQglkwZ2HiptrViMFitQgEIWEIEUACAjckDIzwRqRJGDnONBw3YAaCIETAqL2qqge8/+JDx3h39/NxGN22vTfr5AFOirianQwIgq2r2JSSn46PmYaPP/j47u3dly9/cX2x304fgNPLL7+8uX4uIp/9/GelDGUYDOzi6mY5te1m/2u/9tfevnv9n/0X/9d//z/4I1X743/1ox/86g8eHu6Z6c2btx988OEXX3yhauvaurXNNLx4coPuInJ9df3k6ZOzV3vIcnGx2W5LyjKOo5q+u7s9HdfrJ8942DjQUDJiJBaIMGubPLIIEy3Lst0/WdVS5s20sXpc5vnN6y8eT483u92kh5/96f/3yy++2t083+6GYSrDbm+99Va347S9fnH9/MX+6Qfbi6eZCHu1QF9dONcU83zacA6AZi1MOTNBIJzzUmcpktzVwFiEhQEB0c3VXZlxGHNChoBciiQpPFAagIe2arU5gJsCOyGJegpH8MaSuvbW1tZWVV/mZW3HZX1UmFFWFgxQwC4CiBpgRGXIm+1uC4CmlgsAdaZk3lo7IEMgqXa3huRuUPujgWnvmE4GM0AwjcIJmQAFgFiYhAmJqIQT//YffkCEnCBYgZRSIBoJCSSz5G6996V1ZAN2B2dCJFZ1hAQ+1QURAMCZMiIQAng3t4BAwHAyDQgmwJKHcRw9TK16aMBZADZmzjQwArF1q+oaEBY1oomwCCIGAzFKuBJmwewBrcXx8Pj8xTXS/Or2R8SrKYcPamgagcjc1eZmKwQSJYENs50Ojzu5+NVv//arr74+nebri6eIpS317btXL56+HxF/8bM/K1kckEUur6+WuTHn733nB8fHwz//F//sd37vt1+8+Oi//r//t9//lV85ndbdbvvzn//Fxx9//PBwOGtqm82US6FARtxO42Y3BSiiN12f3lw+e3Zdl6Mw7PcXl5dXwzSqam9NTaeLHSIMOU9jEeEh5WkapmlrgaUU027eN/u9Op7efsngGGD10A7vXn/5NQHuttvl4evXX/0covNQhu1ud3njIeNYnjx/fnP57Pr62fTsg3TxflcFmm1z0UuJ5VDv3t29e/3w+PZ0OrhhgRRWe1sRkZg0HAJSklSySCI6u1FIMueSkEiEHZA5yZgBQJupUVByg0AmZg2woEASzsLSbUakCDTrtS6m0Xureqj1ZLggz5KAmeG8VGMErkxDzhkAck6BnSmQAmBWq10NkRE5LMAcnAO8walp7VEdH4kRkQDO3GsiLISJmejcP4Qc6Pz7f/9bAWHQ1ZvhgqQEkTgzijnNp9ZaMzDOwAQBIQzghWISyoyFIyXKRfYlZcZf4uH5vOr2Vqu6JcREKDmVcRwkswgQRGtLhCOhGZachpyBe/PVTKsfzTTAU0LmLJCJkDBHgJs5VgA8vOvvP/tm5uHrN7/YbCighnOAEI1dK/GZKtA8Iqec+AJQl/rVRfnetz/5m1988SNr7Xr3fpsfjw8P6zI/v/5Aa/vqq5fPnj0zBCC8vLxCR3N778P31Pqf/+wnz54//8EPfu2//C/+q+9977u998vL/aef/fzJk5tlWY/H4263zbkA0Lqc6jxP4zhMYzdlAgy73G3dtEzTOE1ohuF5LNc3+6EwoSMHCoxDurrY77Ybyen6+oaRjnWGCCnDUMp8uE+CMpQIu3/9ctgWKVcT5+0g2npiffL0yTjl9fGRAsJtN2w+/MZ35eLJlMfd5Z6fPrOxgCQpl5KmCkpTxix0tHSqD3dvzBfujb2ns/MfDBGQMYkMw5BydovWVlMFZAhQVVUzBwTsva3zCUDJDAMIz4cGuIKdrGqoQ2NBkdS1mrYkGZFUVbsiBZIRo9GBWImAmUSYApgDCUyNCCOMOURyrTbkAmC1nVpTFGEYimTttfWm0JsvHtVidVgY2SyYiUUYR4K9O0MIgjANhBnQ+G/+w08C0MLUVwQlDAQn5DAKSH2Bta4WSoLMIJyYiXGHMHhH8sI4MNpYchY5R1CIgIi6e+tVO/aaKQrjIJIlEVNKIiIFkHpD14QgZpbLKBkDQEHN1tZ7OLJwwnOWBwgxCMMtwh/vl/duvnMxXrx982p/NWYiBItwga1HV2tu6rAgRoAipJyGcN+WZ9/55g///PN/NZ/uRtqQus5HW/TF1Sfg8PrNmw/efz9JQmIpWVjqWm+ePGu9rmt/PD788Ff/xo/++z8tBXa7Kw9YlgcAQ4yHh8fNtGFBN+/d5uWAYUTm0WudtbYXz56J8Hy431xsN9tNETJbdxdjQJ+yjJvx6uZi2ky77W4cp3GctttdSimXwkQRgRE5DVmS9zWhpuuPUeLh9hc3+4uyvQb2gQOiOcTF7sl22CLi9fWTcMzjZn/zXrl6gtsLoG1OyaIDQV3WstnQdJGgFI/Q1SOs9iERFwMfAs/2W4rzxsbcrLk7YtAv4xlnehfp+cjSw13VAxxZsjEvze8eD3d9jnGglDAUGYO4zofaKhEz5Yiodem6ICuLQlolOxGEW2JFciIG6h4nsyhl8EBidm8ILiTL+tjsKBwizBLms2EDaq1S1YeAI6ITZsIRuRMjYWHcCmWIQCAzJMweK//eP3gfMTVdWl8AHDSYImMKzKZgEeuqAYisSZJIyrwnn6wRAaNzWOQEhGhwJAFCAnIDiiAPAMhhiaCUvNmMw7hJpWwQERRRNoSsnRAFQog8DyQCHuEevS0ekWjiCMRGMRCiQQ/3h8Pp6uqji/H555/9dHPhm82IPmJEBIighak5E1sEQGVgwdGjg9KvfON3X37158vjyyfTN0vsEVbraRqe7C4vXr35+snNjSmeTkuvfXdxfTodNptRHZPwl19+8eL5i/m03N3dfvTxJw8Pd9v99nQ6EtHxeMq5IIF7YyYAVDVw244ZQSF0M425pN47OAKoeb26uq69E0HJZTvsNxcXF0+eXF9fX+yvNvuL/W5fckLEtNmUYRzKMG5y9Vp2FyiBfSXt+eaZQzy+/JwTj0+fe2jEbHmbIOXL61JKHjabZx+EobaWp4vx+YfuBGrhbto5EZ6Wfvcu0UTAYJUxVJu5NuXWZ86URSJQUslSUKRMm2EYU0plGFMeci6m1tRzGgzddAkyFEHhtdVjs8XjvreTK6QRsEgaUtqEofmhtvmMAAtnj3VdZ6Qg6Q7dvafiRE5If0kKs0AnYkQGwK5LuM6nE/rqZKYHiCaZIRyQu7Wl1bqaagXEnJOZEiByBQgIhhjCnBjMXDu4eesH/t1/8FGEhfe1t1o7Usg5qGukpuqtGwZCSi4iCabkxQGQRXAwWxBZEgN2BB2GVMoUYBodgiAwDFoNiDSVcRrLbpgud/sk+WztHGRwgNasEHNUSQwYGSKRr1aXqgQDIZ1PkQGLCC3LaTPurrZPP//sc8lcyiA85DwCYpAGRgC6O0uwk+AwpifDcLks/eP3v2e93d99+XT/wp0MVo26Nrq8fv/h9k44r+t6f3iorV5dX3VtSAwgddHTfN97/+STTz7/4tMPP/rg7u52v9/3Vtd1WZaVzr3BiO4QAURBWBLDmGVd1pzy2uo0Tlmyq1KKlEoZxnVZW1+fPL3JZZfKsNnu8zTtLvbjNHJiFE4lj+PEaQjiNHLOEk4yjqah6wPVPj37JBL3u6+Rp/0H344WCDGMV8N2u7t8XnZ7HsdMw1gmPlW0FS8ueLzQNdbDI58OuB78q89vv/oZsLDDfPfaYznOD9BTHtjNqzYn45xJ0jlZe0aWntv0u6maBZKF97qG92Ag5lA+1OVWZ2cBlACQPAIZQCANLmp9Vm1dSV37WSzHproigvsKeD5gTmroAQHggAgjYFIz81aXtde+1uOh3guRiAGthJby6BHhsc6raTdHACZCj+pRmROCq+vZYC9IGN279t5qX/i3/ugbAbjUuVvtvbnrUHJr4eDu0FU9ghgAnbDkvCPIEEOWAYncUGhgcZEAdBZEBovq4ADs7ghJlcIxnysAWMbNZhq3woMaW+tEAFHCYMgpIiRlohVRDFC9u7nAxDSaO3ED6Fk2293+s69/Acy7zVPBJEy5JCIApN6MoOQ0QTgzJ95N+bng5fOrT652N1/+4i+mjArRdba1Jtw+ffLhrmwP7+51XVpvyLy9vGBJ67pe39y8ffsupfT23avvfve7X3/9dUp5HMda63a7ffv2rVlFJCLKOauquwMAEeeUk0Sr61CG2lutdb/bhfu6ztv9tgyDagdws77ZTOM4IqVxmvYXF2utieWcWQuAlMeUhNjCM3MK7JR2zCNmb15Z83i5zyBJV7fYvvh2SJIIZhmHnWx2mPfoka82MA7tNKMvNA282Q+56LqwSynTfPfpV3/2J5JKBb2/e5UlCVFA9N6ZOOWMSO4R50xqSkRkprXWdVmRUOjcz0FZChGsa0WiVDKnoQUhgrt1UGdrVs2NIRgdKZtKbUttS/OekiOk3gy5hQOShJ//txIRwWhGvYM7Wndr0Ft4mGkLb8OQM2eHQHQSN3ftsK6rnjUYJKYUnokoCQeYWTBn69V9NV+61sDg3/l7HytE7b23VtemqpITE0JQOARA12bRIwKAcxlyGhgGjOLuy1KPp1PEyhyB5tEceqAFkJqpdogULuE0lGHII1HB4HHYFp7Uz88REJJwNe85pSyBABisUdUXiCxxRTghOQgg0OXl9t39u9ZbzptEQ5EsqYgEUQLMtZ0AfcxXgheMW2YRzsL7Z8/fu3v3NaIzTsVoy+MePpz4/WfXn3z96de9HjE8AErOw7i5vbt/8eLFaZ6128Vuhwybzfbly5cff/zx4+PjZrM5Ho/uzpwImYgBw8yISETcdSgjCTAhCs/Lut/tICA8Wu8s8uTJk/l0irCIyEPZ7fdp2KRczkceKaVziRVEIEtKiSgcBAiRIXCIEE8tsdjBSASHvQD1vjqk3dUHQSkAmjYn57KBLK4M2wvev4DDIQ5fR9LoHKpJ53Z6TNM2kxzfvjVdzGyQxAIWRAg558QJCZmEhc60dv/LT0opSUoiREgkiExA41gGyQkJHMUQvB/q8lAP3QxQHGytRxTMsmMce29tjXnpDgautc0KEUABEWEAGODuDpC0gyuaiSmGQUQwMTP3viIm4kQMHut52XMt9UTLGm4pohBNhMPZvEeUzMDN1NeApurugQT823/4kYWbuyu0tdVWmWPaDFkSkjKBqmlTAA5DJpZUEg0cKYDNo65H9I7UAbtaAzJiCKDWTbUhJMYEAQgybXaJJ4nE7DknFq+tuYeGIXTtDTFQFnDOMgWGegMfCu2FCpKp9+1uqv5gfcm8DRVEy2XAoGCTJB7o4QhU+IJ8N8pmyltrdjk9wT60+bAZOOFmoN2YrtuaS07W1nevvsoUGkJI4zgdjvM4jLvN9uXLL65vrlurwzjc398Pw7Db7U6nEwCoKiKeH4yIgEgRkLJEWM6ZJDELC7sFMUkSUz3vlCxCxLVVYnGPYdqkVCiVYbNJKZnZMAxE1FpLJWt3EQwI4DB34eTWESlqJyAqDLp4YotpEK7rHQLL7ppsIcLT8Sge5eZZeIr1QAnk+gO4e6VvXgd1b/Pp7cv5zS9aa1fPnyds97evWRIhAHBQpJJZWM0giIi0K0DkXETIz3Urboig2s2cAtwcEBGBHdXRIpJGV20gjhAES/STPVLiRCPjVCQJCIOstR3no3oHtLUqAgsLEfbWW+uq7sZuuC4BMGBwIHAii/Ag4UyUUiJ3de8BEM7aqa95XkI7A2ZEBgikc6kmm5kbRDhAIposlJn4d/7oWw7QtUG4RW/aUs7TWHIi4o7kEGjd3ZwxCSXEIjjlxJKKSAJoECEphgGRKNwMetdY1+ruAAzq5t4aAPhu3BGwR43wxGW1tvaKAMwSBmtdDdYkLLRJsqNUGIKJOBUm4FQNF4fGjBxMkJgH4vNCnZKMgERA4IVhhJCx7Pbb54wJ3dfHw8iDoAglj7zO0aE+uXkaFcVToTESjsPGA8Ph8urq9evXarbb77786uU0TWa22+3O2kfvvbWmqoggwmfGVinlnOEsZTRXJCLMnNIw5kBAQmYZhjSU0cIBoQylTLsybiUXSnldKyfJpWjX8w3m4ecwbu89D0mb6roSduvAKYN3ICRCCsc0QG+pxKqPGIhl1x9P8XiYH2dfT+XmEpjh4U0kt5vv+eldfvsSMjuNDWD56ifr/ZdldzEvdTk8EoIjS2Izq7UGMQkPwziMY0rFwT0iALsqEiExS5KUAp2IgEL7WltfuxsFE5qjElRYHte7h3pUq/thT3kbymCWQBIPzftxns+GYQAKI8IUztqxN3JjVVf1cEcyyQgYqhoAgFh4CpdzC7g7hyVVgmCO5AFrNUCICHcnPCe8IDwggjABSAQGuHDm3/2jbxtY7dWjBxgRJaGcMQuxODNCoJmphRsJj8wjx5SEJDMCWFQ3Z46UAQJaXz26ga+1nvXutbbevSucjsexlMRDBCCimqKkQIMIQQSUtaq6DuMm0U7yLg+5FDNz05QKcLK5HkoRYXELpom5FCYR8pBwFBGIhMYIJfNWIS5215J283wyOwWEGXY79tMbWON68/5lfn7/7s5C+7o6hSp4IDDlnI+nUxoKMiEhITNLzkUkpSQPDw/nMaCUgojMfH4ViPAwDCKCHKVMAVBKZiZOPAzjUMZShlRGljxtdpKnzf4y5SmQmBkC6lpLzhFxhhi2WomRiLQbATFSPR3IAiRbAgGFCkRkzTiDOerpRBj17ZvIl6kkwXWz3z2+eyWrl82VP3yBt3eRoezHevu1rSeXApun6/Hx9ad/RkAfffLN0+HewpGx926qgDhttiUPKSUkWlqvrXkASyrDwJKGcZKUkcgRCfEMGzENQjbzTl6bfXV69/Lwxd18j8QMjEhcBjLurbuiKdVe1zqrsTsmZmIBIAgx/WU83cwCOGUi6gCGAO6Y0sBUUMU69Ro5bRGS1e4BEGgdzaO7gRMgIrI7qgW4AKBZJYZcBnNAAETk3/373/Bw9+5hEcbIGD4mGEow2flgvCubinZwo8TCclaHEdCBKkK4a8ruFnb2PaPWuoYzwWCOrpBIwNHDt7stgUhi96phOUv0TsSbaR/B5kR4sdkM46awbEuaPI5dV0YxrxozSSs5Ew8WiUhEhHnnFuYLSxAWorGkXUo7xmSNSioBq1mDMAzDGoIgVCa8CUW1GSLykAGktR7gwzhZuKlxkt4MgS4vL1V1HEcAuLu79TNo/C933957RCAiMw1jbr0myeAwDiMhEyUiSXks4yhp5DTkYczDFlDKMACyh0NAKeVcfjcMwy8pnsgGFuY5j+7UtQqQ1g6JlxqgCrqCrsDS55lQlsPij6tZXZZ3F88/8LXp4XTx4TeIYtEFy8CtuR6ASaAcPv+ynm5HCShj87Yc3pQxy7Q5LtW0ShqSMBOZegRAeLgLJ05cShHJiDSkkvKACK01itC1mSoySy6ApAjHVt8tjydXA0mZXfDQl1618AgxmNdwbdrm9Vh1/cvJ3hOLOyBwIAaeN09MOQCdGdEdIYoMiUbyYkrgZZnVHROKh2GEq4WLurujeYADMQE6gAI6IjE7gDNzkgzQmRb+m//wO+gK4ZywayeAQWgqVPJ5GXdEqZW0SkC0VplTmiClxMgRjmQOHtEB1wCIQCRWaF0XhMQ4QggFCkCRsesqGa8ubihaIKqvGDrINmJIKY3jxFBYylS2u+2Q0z7R2Na7pd6b9dpnLAfkdZAx0RjIEYyYhTMSAZlF8wCEQfKWeEwyhYXbqrb0VkPXrFaUw8WMbzY3po2FGaRDhHfroW5du0c4gBtAwG67ZeHXr18/e/as1np/fzcMg7sTEUDUWpk5pRQR2+327u4egYSTu282G0RizsJjhAMCsnASTtyqzsssORFia1VY3D3nfH5f55zNLAIg2PuCkvIwrfVEAMvx0KCXvGuntR/febhEPh0e13mWcVDtEauvtcFu2l++/fwXZDHud5IxdjvYXXQP//TTTJy2w/2XPysMl/vN6ooAx9t7zhnPijOfEweeJeUkED4MhZEAgZHBnYkxoLdFWyMIYhiGiccxROpSzV3Zl2XtnHPZINKhnR7Wx9r7clqFRsTc7VT70WKt2iE8ZcuFhDICIQJAISqOlaQTQ6ALCRokKYyZcQDP4OJ92ztH5NY7AJpqRISnCIogNSJISATguQgLSAJCJOqAql5ZGmIP7/w7f/fbSJ6TQGBrJzdIlFMiJrIIC3YnM6jVAjykA0umnBiZKQKQXLV2q4k5iZidx8iCJgHAiQjZw0SGgQcMZ9f9JgWqQx8E1IzLMA0XGGWzvSjDTjilnMbxWhIB+Wp6ancP8wOwZanduxNO44YwBxg5uSFIB2TvpL0iRLikNBYawKG37npCXMJa682C0WKgMqZxXVp3W1W1e1tOa5sREJGACJgNHJHGzebd7a2kNI3ju3fvck4RMc/zZrMppbTWp2kiopwTAByPp6dPXyCySBqGgggpMWKoKiILMyFCYK3V3Xvru93uPG8gU29dRFpriNhaE0R3Ph5ug2MYUgSsBseHWzydaBwYCUxbU5CSRPq6hKth3ww7nvvrV7/g3dPtdtfvfpGYeRxtSPA4D9Zgm4/HhySYzN/e3hGsu5S9ahBbMwEHYlcVlpSympk7i/i5bIxRoasBYwIkYMop5V8mhCUQw4OQSOTUewsolEDooMvt8f6+HcwtPB7aIcLaqms9qTeN1uyBkqckOTHnzsyDjCKORMSFsAtFochYEAtyCc/aB1UyJdNAQLPWdFZE9YSEiuCujCRp4wDElnMqqUwZMpv1rjGrPyAZBNW28G/94bcNjRkjelN1jABjDw9362ChqkvrzQ2QARjBS07MAtwQFYHMW9NFchrGAaAAWS6lpBEDCfIgm3ASoJy4JCmJMpswRSAlMesUw3b7ZLe93G2vpmEaeAsogZgTReTa1+73aqd1Nce1ZEFHBk95QN/oCr15XRU0XKsDEIl7qPVEOaWi2jBVFO12AujhNOAWzMCIpbAIAUCY6qxaXTWAUh571drqkIfd7uJwfEwpnZs8ci6tVUTc7XZn3ZOIzGy/393e3k7T5uLiEgBKKYgwDIWIAICI3G2aNuflp7U2jmNEnHfrZZ6ZWFXjlx20wMzz8SRMx9ORWFRDax+HMh8Ora4gWIYNJ2IA1Zo5zrb91eLx9Zc3+73ff3G4ux2f3hRTd/Ccc5QYsd/eZk+8Gd98+hfQT9shHw+t5PMh0cwlgCg83AAJEFEk51yImIhTSpQykRRJTJAkMQYxIiEAuhl5CBIyaYQjdLC11nfL4/3pdvGTdtU1FrWH5aG2amhrt1Vnw9nCEDqLG4KkYSwbImchEnVYmCCREHLmHWNBl2hkLbUWvau7mWs7m5WBmYkDgCQRZ05MkHMWKSWPSEEMwOGxmq7mK1BtutS28O/8e99NDAAVMLpZ6x0VQEnBwhyDTGFprWonTiXlgAbiKOAOESbMbhBuZ6CquROnsWRBtK6CpQjRL60jITkPIkI9cwJyRfIArbq7uNzurguN23FTZDCw2g/DMAhtq661PyTauEt1FbIBUzeAiARD71pXq1VVV+0nDeU0Mae6GhgDetDi0Hp3BttKGmKnPaY0XO6frR2SsCCaWq/a2hECx2kHQWYuadhvN8NY7h8fRCSJqCoRETEi7XZbRIyIw+FwcXEREW/fvv3ggw/GcVJtKWVmGoZyXmr/0syYe+9mxswist1uzUxEkiQRcXf0KCkTQU7J1NxUWHKZUh68tpJ5GEarrWSRsnk43A1M1pdzVqv2mncXeHh7+/rrtJ3w9VenoVtN0taBwO/f6qbwuGs//yxf7XLZ3r75bL/ddI95PlxeXQbQ+adEQuE8DOVci8a/zMQwIkoeBDMReFTrioAA4IgBzJLOs8tZblTV+4e7N4+3Xz6+fmfvVl1MaXVuxmRlsbXqY9cjmoF3oUBCSCTEQ77IspUkERCg7p1iwxxuASbohWwMZ3BE7na2gHoHDw+CiMRBFMxpLERIgMhZSiopESG6EwucezHaWrvX7os24N/9w29nJAANDIvQ2sIUHRiZiDy893P9Q7gTCUlid2AmxGzNExITQyhQTykzA2NiTkCgBg5GbBoKSCQcCuaWsucsxqBhQGh6klSeXH/MyELKnEB4WWdEHdIIzn3poZIoK4TZCQMwuPcIS250mltrrr1pLMFAiZhFuy7L4l5NK7oXSZnDtHrPm3yzLVuWTe+RCMBj1YbQWHjgXXgCxGEzplR6V0Bf1ppSmqap935+fm+3WxE+Zy9Sytvt5vb2NiKeP38OgEQ4TRsiZGaAYJacs4jkXOZ5Xudlu9nklHLJm81GVUsuwzAAACOx/PKtUoaScrIeJUsep0Tg1jHn7VDcCaiEzWgNkefT/eHd2/pwC61O1x+ELcmj2+Pd21dlHJPoCFGXQ3l1L1eXs8Th5RcXn7ywY328ux+ncvv21eHxAQHqqkSp9zBrZhYQAAgAqm1ZZmZBAHfvXYFZJBEJUpIy0TCgZMgFUw49i8Q9p1R4ApZTwHHt93V9jAWJCo5MZAruHcITUSlMxIllHIogCpyB1NJ6QwZgYA4AD0WCoioEKYDMrVtV9bOFiZCZA6gLeaKUExFREAOFJEhEGIRozArQe7PWrJm6OYLw7//hN0kGc7Oo1kAVmQABAjCCurZuFcLB2FSIpKSRoYQJsmOANwRs1Y8AAQREOdwhEuMekbqtVWuAAyKSIKBZD1ZDVeiBlHLiAnWpT64+LMPkFsjMIofj4bTcl8QCqS6r2kKBzbs6mAECmsXasFssq87L6mc2hxgLIQQhhIdrUPBmKARBQQOPg2ywKTqHc10XbWutzSDcVreIyJhyLkkyr9p602HaMtM0jiLUezPznPPV1ZW6ujkR5Syquq7rMAyllJRkmqZSht41wol4u90yc855GIbT4UhE0zRtNpsyDOcbI4lwEhYZckosKaeUEgBsNzthQTdMQuFjkVPtUBsm7mYDg2CoKQCu97f98bUd3sT2xT7HyFQ50e2jP73c7i7i/gEKeL3Xh/vyjefz61ft6y+E/OHudWF+7/l7D/f3h8dHpjMImFpvACAizOLu7kGE7gFggMBpAzyypADIpaAkADc17drmuZ8WwkAmEh5y2U6bnexG2iGIL7ZWw4iuABCZp8QZLDLnnAk5hAkdAMIDATEA1LrFwrwKQii4QQQGqEc3601VewBChAEGIgpFKgkBIQIZDAzIkaLwIMzhAU7h0MGq1lobBCIG//4fflfS+TjPm3qrEZE6NAfTcFULM3OvHcx4yKNgHtI2UULo7t6191g71tYbECADugiO7hyIKXEL7bpaKCKJYIQ3O1NqjYIDkVOuyyKSd/tnvUv3k3lb6vF4vHewQTan07H1Q0TvWps5YGIvHmLhpt7Va1NmlswkkAQyJ4osQQjMkYVlzHnkIsrcyLr1lTKNDEFhkhNCoKsFMyElQhFg8aBpmDa7C2Fprar2YRhKGXa7nbsv88ndck7DMJyX2lLKMAz7/T7nTMTrugJESmkYhlLG81p8eDwg4sXFRSr5/PellHOxRinlYrdjIkLMJaeUmIUhhiJB5NrAewC04yN6BcYAavMxgQogdns4fdnmd6fH14DTNBTiQhwcZtNVX9oorZrC0ktfp83WaxC1VOT+9btxM7z48NvdgsGYErALp5wLs5gZIqWUIoCIRVLKKQ1jygMSnO8H9Y6tm7a2zNotmNQMCYPAFJEIHJfezcMpz60e6mG1Tpxy3o5lHBJJwgDq1psuCGM4ErBZBNFaK6K5QUJEhIgOCGbnHn82MHd3UCAjIiYONGbIg3jUrrMpAKqQAoYIETEBeFAguAN6AZtaI/4bf/h9RiSkqmruENyqdasGyojnzv3WvSuFZ6HCkBHLOAwJ87rqarMjAECEqndmy7Q5t5Z7QPcAwN6XwBborhoIRIOFCgsgIXZwV4W6xma8JpTW57W11g6n5dVpfiu5qOvj/MpwIUGLzoxCG6QxAtGzKxPlUspmWyQjgFMEBCUWCRp4HFPJgqqn1hdgQUMM2U6TIOY8giNhWG/mRAwgkNJQmyGloZSUUm9tXeaLyz0imjkArOvKTMx8lnGOx+MwTJvN9vr6ZprGUoq7qVopeZrOl/6Yc3Y37V1EhuF8MjYMZcg5J0nm5m67zYYJiLDkMg4TANTewiMNA7hpr2MZzJqtixNEB9F1fny9PN5vtyMP2fpRMB9jfPPVT69vLgEXe7yTka2tfJiVME+Fmt3d3V2+uEAQAJ6SPBxfj/ur3WZvbQFAQBqG0cFra4gsKUUAICaRcZxSyua9t9W0IaK7YxgLByDnNGy3XAYkYsZ5mY+6PK71sa69nZqvjSNxUsCzST4XzllIpKQpp40QrT2WdXU3RjZnNUyZ4Vz/p4swAMHasC6m6moKhIgYEM6RSiCqR7eoMrBHm9cWgUjGrOcm/ZQyMUZYeDCMDFPvqI34r/7BhxjE4u6KMCJl1VYXd9eUMNxMtWuE5XPrINEguSTaZ5qI4NRP3WpGDvSIjkiFBqFBAZdqtXlrVa01O3kEEQKFGTFHpoETMWJiYOJ6thIOAzh3PS3t9bx+OfdXD+uboHWtt2vMxEAkwhNiJmIMZMCSRpYJAYYxDeNI4OCdiBNkQRw4ZWB2RCVBRrdAHGUz8tDaend3Nx8fwLtIlpIJZZou1ACYyjBo0812j0gRhgStNSLOOe92OzMtJa/rcjg8DsO42Wx3u900TcNQSsnruhJhKWW73Ww2W1XbbDa9t3VZN5tNzhkJN9uNJBmHQUAkCTNBGEUs8zxOG2ZJSQCgu6cyDFm8LSyci9wdjmYBy2l+90rE3r39+nD7+vnNN6bhao1y+eTFfoC1na62+7s3b2ebLzfTmy8/V+SSi5fBtD68/XLEfHh3WwZC0sO7WwmedhNwECAlCYiuqm5IJELh5wA7LusabkIhUgIoJ8nMLiK5SJ4QWRwyhvYmzCRsFOD9sR1va333+Hh/fFAwIiHswpalDHJBVFhkKE+n4SlgrLMdH9QsUcokRJiBrGrXcCcOS717bWrnJRmiVgfCPILHCmHA5G5q2E2AA7gjEzqYAjNGeICzZ6iTWW6tRg/+ld/eSmolTQQlQkkyYNamvZ0CMcKsGcbIwt4rwjhuXhDuBRsLQBJVnk9LQEOMCCTH2mdiRsh9bdajtnVdFqRK3IgyhrA4kgC2jJvEJcJKGrNshYZpmFwdMJb1/t3Dpy0el/WV2slxcegsIjxk2TIrsRImiMqD55wpIokMY5J0Llh3MRRkYhjSNOV9kqymgY6t9WphPJ/mPPG03XAeh6EAJyY+ridAymmbUpmmYdzuTqflNJ8AfJq219fX2/1mmVezHnEWN6ftdr+Zduec6ThOqn44HKdps99f5Dwg/vJdcTyeuvbtbjeMg4gISxLZ7XYOxsSbaYMkKedlqU37MIz393cExuitNqHIU5lPc6gJRiDbcljuXh6PD0m4PZ50OZnPRra003aYIKA2e3J9/fXDLebtftzWumrXcN3fXCYqt7e3Bu3h8IAhJQ/delVLnIack7C6MSJZoLu7qysLG5qDj3kYNnumIpxAEiYRLkCiqtYag4VrVxXmcSz7sjH3x9pObTmu631bj8u7xY+SJfGUpOz310Pe5DxspqtpfPZk/8Hl5qMsF2vtEWtKMeVtBENY09U6IBfy7CbBiJDrir2bqw1j4hwGBnEe2AMwWm9IDdGAwLFrawRggOHZvWiHcFy78R/9j35zXu+RgLBoPFowwkAYx3WtNQCxGwOUgSlF7nObhos0jh4nokiUrLV5bRoVCZkYkLvVFiuKqOl8OnTt5gpYOQVA5JSYJWfJnCHOChJ54GazL2mDIUHVA5uth/rZ2m6ZOaAjOlBPkllEiIsMCCbihmDW0wAsgSGJi5AydHVFI8BMzBBs0df+QARqHYNKGsayzWlMedxd3ewvngQxus/zDMz77VXJZbPZTON4e3d3mpdhGKZps9nsd7vdfDodD7OH5Vxubq6HYdjt9iUPj4+PImmaNhEUEeM0XF1dIWKtNec8juP5RExEcs45Z0QUEWYeykBEInkchyRchoFJEPHweDo93p9O9xj9dLjvtZLberz1tm4u9g/vXqIeu2oo7Db5NN8NeTtsNpfPnr97/ZUEOMLj3d3z3RXsNoSyITkux+P9AwVt9vu+rmfu9TKvCDiMGyGOCGIaNyNE9F7P2Vx3Q0JmIuGUEhEHgKQsUpyQOSGNGAhgFBaoQZByypTnZZmtAaE6LqtZuEVd2qn1WXAQKYCS8jiO+6lc7IaL6/3T3fRiv7l58fzFk+vnxCkMwpBZCLC2qtocnVyYMUyBEoG4KaK7RcrEAsBACCxFeJKg6Obdkkh4m3VxYPXcDcMhKtmqIMb/+H/xHwHmHjNGD4DAIPJo1HuYLwgcnhOnIQ2F0rqstdUybhwZ0AONyDRwWcGiiggTd13UV1PvVqsdtXlEqHWIhOS5gPAAkYdpV1IBd2EDaACRc5acKBgxOA3dlqXdw1lmxSU8Ukopk9lKngjEcaG0dp+RoWRBV8Yu5KpLW6sFAYgEgbtQImTvtpG8374/ld2mTBQ0TVMZRlXrZr21nNL24hIxDeMoWY7H07LM24uLlHiz2UzjdllOtc7LUlnwxYv3NptJRHIa17WllPf7i7N0yExlyMOQIzylLCIRkXM626rlLz9n4ychMSd3p3O8gHkcxpwzURAFU4hIqNX5BF11eXjz9ZdVvZ/+fzz9V7M0W5KeiblaIiJS7b0/cc6pqq4qAAXAwEZzwB5gbCAGYw2bJo1Dwx1+NmnGGRgITjcaXVVHfWKLFCGWcHdeZIM/IC8ibVkI9/d9nufz8x8Pu124B7JjUJa6rjHF/XH/4w8/HI8n7a2dz9M3H5EZbmve5XXd3p5fAHwY4jAlYhbi4+HBmVKIjNRa3crm7gAIjgjOzDEENzdVU2VkIAKge0IQEd03g+LWGRHQVTsAovOyLKvW0uvdr3gt87VdN2+KSp6JoGy1V93vT7vh8XR8fDx9eHr45W63U3VEDiEY9KKXwIEpOYSuDtACOoc7rI1ZEByYhQitdXSNFEKKUxoS5eN0ipyFBA1Q0Zy6olEQjFbJ1ICQA/C//w9/ISGZ2h3t5lbBCzivWzdYA+eRhkAUWAI6aF+Wa3VNaei6IqLEQBST7KEXtQ5oJM2sau+1VUDw+0wZGD1ygBg9hoN7BqDEE5E7lBDFupljyqMwAbrwbj+9a03n+cX8bsyM7gQAptZajyGFBEXfHJtbSQyoTa0TA4C21qqSmzIyQwgyBhkGignitth9KAWuHLiUsm4rM4J7jIISEYMT1ta16+P7D8M4IGIIYV4WYl/XubX2m9/8Nudspjln7c5M7949qXYAv7PrDofDnaAaY+q9hxCYaV3X+/44/t3R8XEcl3lW7XhnyBBZV0IUCdbatq4A3lvp9Qa63d6+WL1aa7UD1EuwXtcWhU045OxoTU0bELqwrPP6zccPtZcvL19OhwfdylKW4+EhT1l1i0G+fPmcJRDdl/6KSDGlt+u5lXaX7tRSzJyIAVAk3K9MJJAEAHQ3c+29AXYAd3PrioBu6mZdKweupoYahqE6vt3Ob/P5tl23vpq1mMSd56WklN89fHOYjvvDw+nh4d3Tu9N+v5bLWs9bvardqq5ujeMQZHA1dKMYYsgJBQEYpfbCQUXQoYcg4zAF4wQhSE4hZxlAoakbiAOjc/ABwz2IEgAAgABJREFUPTlAzrwbB/5f/8Nf1K2WvimoozMqQDEotc5l8ynFMScCziLQ1HtX7dfrCmQxCqKIhH08naaDEJRaFE0yqHciI0TyGCQzYAwBEO/hZ7QpxiiIrgzIiiBxIuBWN0aadk9aIaVwGE8pPMzzee5/QCL3pM3c75TvGEMYx5EQ3DbtFZoyihvdZ2G1t7WDAwEDSWq9t34Dw7J21XVIE1MCot7b29sliBBp3dYQhEgAaNm2EOLp4THl4Q7iijGa+VZut9vtF9/9yfHwWLZyDwKkmB8eHnLOas1cW9v2+8N+v2/V3LC1LsIiZAYAGEJ0NzOLEsq2xRBSSqrq7q6OAGbeu7IIe7m+fV2uz6+fv6/zC9TNtq2tNxIep918+RpIoky11QoOBENI0+5BeFjm85h4Px621qfjyXq7vF1cq5uWUtEtBSSknId5vg6HvQzRa6vblnY7jrFti1nX1pZl3ra1tara72kIkRDTlNLEIrWru4UYWQaRjE6lVe2diGPMHIQks4e16epYnG9a3rbb9Xp7WUstNyYY8h5At3IZ0zjlxyGdTqfp/ePxafcwplx8u67ny+W1W3Wv3RoCALE2N6fgcH9aClMQYCARQG7ddJAp4w6boMKAOXk27zebGxTRIdiAGBDvf3yI0fkv/8M/d4Dez9f5or4CbA5bte5dGfIQpxBEAgTs0NfaSrM2l760th/HadoTxchjkpzSiEBmqkYhjHdtdQyjSCIgAkJI2t3MVf/u8UoYEcmZwYihmy9bvYQx7ManKCnnGDg58LKtt+UNwO/NhBDCmPfEwgRRAgN2cweOMqEzoAJBB3KXqrCs1aA5NAJHp5jTLsReMXA26+t2jSET0OdPPx0Oh9ZKKa02HfL09OGd9u4I7iQSUorn86WUbRjy6fguhACAZStB4n5/ZBZmMvV7WyrGNA47VRcJpVTtutvtWmu993EcibCVGmMkomVZ7jsEAKhbERZVXddVmHrfEhP08vrzJ+pNe3Fvy3oWkvfffGOtqcGw28cxOQA4tG1hxTFmgC4sAqG1BXB79/gNMK6Xc12Wray7IVzO596cmIZxWGs97g/WmgOUZozctZEDs6j2WiszpzSYmboR8b3SgBwkxJyypIyB8e6F0O6ITkENa9VSOzhU8tfSfr6ev6wvr7fzeT5f2k1t04qJJyEq5W3ZXvMUxrwPQYY4pTBKiC5evJyvt/V8MewdN9WNGRnYu4f7tBWRmKIE4RiYAYr2Ci2N6QDApEgqDgSI4KYVXdkgCqecYkoJCFg6//d/+V1vDRHVfatfup7NNrNOYdhNuyA7xhCjsKv1VbtdS1taN0e0+u7pXUqPWluKY0yx+VrrRsimSozERoIIBN5bNVPpiu7gBmYoIZOLO4E7oSK02m9LPwPEh9O3Q5wQBdFaEcZTaW+lXN3uyWFJYWRgJgsUCcatFgIhGwgSckPuJCI4THkfOLFhhJhpv097AtHZGIfdcKhliYFjGi+X+Te//S2G+PpyjkM+nZ5iGtZS73nY1g3ALpfLvZ9xOp0Oh1OM8Xq9Xi7naZoAcBgGVXM3Mx+nUURqaTEmRGytOOjxeFTV2+12PB5jDOu23WsAOefL5VJKGcdRiLdtu2dI5/m2zG/aa0qS89jLatoQRYiXtsQQhfO0fwBhDslV27oABtJ+u74AFQfV1gLD2/PrbVv30xRFQqD59RnBhml/7/d2teN42OaNAqMEoLCViuBDHAgREKdpF0JExJDi8biPIQEJIHEIiAiAgEhCAICAEqKEKCIsApGFhWLAIXWIt9aX1rZltb4arYTNzIEZ2EhwW2+32yXmENNQ+naf7oNhK/22Xa7Xr5f1FagEcAIcJAlFZuEu5Ny7t+rMjOgSGKj31c00xaCqvQEgJpHRd15jAXdCoshOYw4xMzrw//nfvut97n0jaGatta2UeUiH0+5dTo9CMYAwRkW9Lcumm6OoCQfH1hFof3jyBinlHIauW7OuoARNCImiO5s10H5vZ6qBu5gaIhBR5ATAgMrByUGtFH1DSCmcjrv3wgOilbVqs5ymstWl3Mw7IUzxFDEhduHY0ealo+XIIwdSWJmVhSXthzgOnI/p/UCn4DLRIelweDiM48GdVHWcJgP61W9/w4H/6q9/Pwzjbn/oCuaSh/F8eVWDlNK8XM/ni3a/I3xyzubt+++/JwYJPI0TACL6vShzOOwB4Ovzl/1+qrUCGgCYdWK5XC6InlJa17XVeq9TllIQEQmCsJu1VkLkEKlu27as58uVGFq5mRZwjJSmh3frsrpZzhGYa3Pv1sqKpoenh6WUMi/WqnlNIYy7w3I5m8M636Zx2A9paR0AbmUOJPuHx8CxrU3dJcTIUf0u8y291WW9bduqVh1URAgwcGBm8zukDJjFCMkQ3AyBQyIUcqAYKKUg0NyLgoLwuFPJt2Wey2vTpWxzVwtRYsj3eNltfr0t12E45TFv5SLeoKC2dJ0/f7r+fJ4/E7ZD2IlEB0B0I3AVVRIiVCdgRidylmRorddaN6JAIAnDwClyThyZRNWBE0mUSHEIMez4X/zPJ/S+1Vu34g5t4zGfPrz/3WH3jkiY5K6oKFaqtWamXtPAOQkA177EHMIwdUXAWnVmBodq2gJGInI1741ZEAhpQ4/WHcAQ3Js6aswi4uANEQF9qzcTIN6Jh308csRrmedtc8C8G7bttpUXCp0wjnh06wYKZtpYICJEhMAxcQRGDXFImFOcyIJ2R4IB82F6aLVebm/bsmq3pdfj8dhr/d/+P/9pl+nDh1+Ow8SCQLwsGzAMw6DWPn/+uff67t27b7/5VYqTmb6dv25bfXx4ynnovRPhfysHD0Skauu6xCjmXdWI6OXlOacM7ssyt1J7a/cHwjRNqm7eGRAMXFvV23x5gWa7Yextma9vaMToZX0TcgIZjjvrVtZ56zMBCsQGTO6tb+A8DGMI0mszw242DYM7MFBvvZWy2+9SzpxjN5vXZS+SxnF8fKit91YVe0RSt61u2ly7resNscUodavLWrd6064Ajuw5DchE2MDYJdIwObERAKNrr9dbLTOadxRDu97m12U9t/N5/bz015tv0BU9GCICiTMnP18u67I+vfvVmI5aO2IPARKMa9+eby/rep7G/W5/SkM0BHAE27a6qBuTSBaArmrIFCMR42WZHeQ47COTAjBJCjHlIVAgiikfIkchjpz5X/zFd0TctNVa61a/efer3/zqH+937/bDKYaxtraWsm3bdbkCNxKrXhB7jJaGEXho3lLMvZXar9abmwKq+RoY2Mm1GTRzRCBQ7DATN8GEdGe5gGQHaODA4EigStvmh+NjUyXhcXpqtc/XZ7ctBMqyX8u61YIAoM3Num6lrIyaYkaLrVHgGCSEEAMbBifide1r20DbY5rW+frl7Y3JAhBznHZ7EfnjH/+WyfaHo6qdLy/X69m75WEc8kAhllIvl8uvfvXrb7/9bhjGnIeX1+fr7ZLTcDweRWRd15wzIh4OpxCCmbdWr9fL4+MDEd0bw6VsxJJy7q2eL2dTTSmZGTOnIZWtEGCgUEsxq7Usb6/PGW2Zz+YthkjgrhsB5JQVybqTcG91vVyihDxmQyq9+VbaOk/TgBLcMabYrQASIXZttW3INORsBjkNIQr0bgYkIcWktbibA4sgMy/Xq5nllAAMzLvCfc3DIhyjA3cj5khMRgToCAZawDqhgyqot7ppbw6hlHor22LtbXtt2Mxqa6oV1ZHJ714w5hQknG9futk3T7/cxSftHjLnfNzhSZ3WOm+tHHcfng7fHXfvGGL3rgDuWm3ucAsZKZg7IFHzzaPPS00yjnknRELOUcKYA6dAQ4hjTjEwt1r5z//tr1LcIUnvGsP0y++++/D0zWH/8M27D6fDw20pX16/Xm+Xsl66VRNBFmIlZmORtKttU1NEWtsCWkGbgYKbOJJZ0wYAGOOYBnTpvnCEQBlRQsIQDaAToamBAyKypNY2EXl8+OYeNg6Srrdr6YsgEo5DOpryelvNZomKKKZXU4XmxMLERJLSMcpjjGSka12ZpDfYp1O0aJ0Oh2lI0zQ+pjQRwjyft+VGRAgwz/PXr5+Y5enpm5yGtW7zPL++vX3zzbcfPnwY8oiIXdvr62sIYZqmexaolJJznqb9PSeXUnp7O//000/v3j2O4+gO7l5rI+aUUuu11UrMTBRC2LZtHHNtlQEDkvu9S62MBqW6m/UeQ+itgnqvlnMywOV6xSBTylrr5fYG1g6nKQ775XoODAiw3x8lpK4tRB7ykHJct62UDQCfnj601td1BoeiRkTo0LZCAmamrde6rPO11ftmk0IUlgDgEiSlabd7CDEgGTIQM4GYtvl2afMC24ZltVoQXVIQ82WeL7fbdZ6L9s7UwVoHFOu1bmutagzgQObu7kRitj2//MyRHh8+julY2kIJUxoIU+BoFdGHaXo6HN8fjt8M4UAu4Oa+lLJ0dZEwZDaMZmAAata7hhiGkDMloYzpPsyFYQwpBLeOZvzf/c+/dKcY9uNueDh+mIbDkIfd8PDw+JEgeMetbD8//wDQ0IM36qrmQDI4ontHVnAjHKu2risa1LIxOpM32FDElEXifjeNOaGgWQYACcQCwoiAgPdVRkIMRN5pMaN3x2+CpFYsCXf16+1q5hydMI3hSTitZenWU5Iowg6tnZ16Co9Cjznu87QLeVD1akstNVqKHKBzohTvGTgMDrDNt+v11b3vjvunh2/UNCV5//FDDMPPP39+fn52c47x8eFdTgMRuVspG4CnlI7HY+99nmcRiTFP0zSOYylFu/XeLpdzjHI6nWptIYRaq5sNOTPh9XolwJSSiAiS9u5N13WWYETuataaMCECU3DVVpe+LYTEHEtviF3r7NDILeWs7uV6Xa+XkMI0jutWchoIcBhy601bJ8EgMUrMeey1t9am3UAEw3jIw7CuG7h36HfpGhGWsrVtAwB3W9al9Q4EEiRIvINxtTV0ZGJ162aMEkWIiEQoJY+RQgQHkjGkuLZ6vp0vy/Vtnatpd6+wMOhatrIUUqKQCFHdTDVSLG39+vLzNBwOh0eiWGpl4BgEIw3jCSAJyzBMeb/LeMo5db2peopTt1pdOyhwZGBSFJJmtfU2yDDIgA02aE4GqAjq4ube68b/+v/2pyIDUUZuMYQxHo/Tx9Px0dxfnl+vt6X1frt9ZeJBsgCRDIEzAYYwxiDuUKu5W+tL6YuDE3XirthWXSVOIpNZAaAQhxSTASptQk4kqndgHmVOu3zIcUwpMWar15Q5x4+9Y+sVUa/X56Yt5USMAYY8HFjSPBfwJQYSjkhcqoPvh/TEklOeOEXEyE7eFJq6qoMR9Fa3bSnb1su2vr196W2bdrvHp+/m27wsZZr2hPzz50+fv3yedrthnETyw+NTShkAU4q9d1VNaWitLsuS0oBIu91uv98Pw1BrVTU1nedbSiHnbOZEdO9GqioihBC+fP0SJAzDEHPS2r1rrUupM4Jr79r7us4gmFKs69xKMW3bNqeUQhLrAGgM1lpHDnmI96Lq9fqy2504jkXbtB9brfv9vtXeSgdHbZpT2u/3qrqVNQQSTmkYpt3O3VvXtRRtat0kCCFKCCwcYmQJ2rqDpTAgkgMIJ0ByUEcHgBAjh0DCGAOkgMzMYm5gxsIppyFGB7osda6tY2/QujZ0DZjQGRES3YmMShLywOX29vnzM8dpGIbA3NSCAACkYTrsjrVWNGChHJJ7U9PaCmMP2YFQgda6MjAqmiIy1a5qLiE0V/PuTBi4aSu9GUIphf/dv//n+/GbKGOrm1kPgd1xSPvz8+vL9bJWu93O6/w1RwohMksUkogSeYhpGlLKE4Ku64XcDajZFiI4anPtbuodKIiJOQGScACoIJZCJBQD7AYEIWHeSY6SmHIMwdXcfDc8mM5O2tpW61cCZxoIgjmbckyRqWpvCBTTPqUn62OploddlIl5iJzUnWAgIENwZUYKiEXdwXfDGImijO+evjkeH97ertXK6fDYi8/X6/PL1zTG4+mBWKbd8XQ8ldKGPDjA9Xph5svluizzbre740GPx+O997iua+sNAHpvw3ivRMK9A+nuRKCqd+7D9XIZxzHlaGoODtrn+TrkaK0KQ9OyrbMwpSTrUszUdDXTECLovWQ2qdO8rW7NtATBKLxu7fT0ngTVFEOgEBIHQilbNdNaSwiSUuzaW9NB4mW5IcDxcByn0YiXZXX167Jeb7O5SYiASIApJkYy15Bl3B/SuDcgQCJOEiLJfZCN6G6lQuto3bT3puBQWpvnZe0297K0pcMKIRlYihhDJGAhGTARIgoQh0ApRn47f/n6+kkiDvkkxFp6jskR4pDGYVwuC5RNUZ2paCv9WuobAsSAoM1J3N0MwEzBOeZSdNbeIxGSgyuCotfWWinqyv/mL//JkA7TMHaDZb2pL6+vny63m3l6fb0QWKnPt/kzJTcwxgBYCSHIxIzMKETMThwFM3NSb0hgDopGrKXdzJ1hD8BAPUhEAHREgO4dnBAENQiwIMQc4e4Ehr61MwuIh7YtAEudb117EA6yR5PemnknLjHuhAfC/Zjfx7i7rWu3mofJKjKDWtVmah5CEM7aGzRNeRiHA2two/3pNEzTttzU6d2H95e3t7fXr73PXdfaLA+H+8oZEEk4xPD6+hwi3+brz59+lMAiIcaw3x+GYbjf5i+Xi4hM0/T29jpNg5mllO+pB3dX7SGEVioCoLmrEQJFYUDrtbYN0QM5kiM6qq3zrdeSYlJ1BHdFQkZs6sAcQ8wg1FtrW2m15zhITGqac0wSOribszkRM/NdtbiUBQiCxF47Bh7ycHm79N5jSikEdOhgvZTedV3Wrj0EIcSU0pjHNOXp8G46fiMpAnWOlPIupAFDsKq9Nb23bh203NWpcKvr8/Xt09e3H16+fJ2vSylr3TZcQTwxiGAMkVBcGSmoR9OKfi+T5eV2fn15NoxDeohxp96YoW9b4mjoL5ev523DaBzNvDbrBtq1NKvs9yqPOfYOLQS6ryMN0RCIEaA5Ajj4Vrde+S/+H/8DoEuMxPB2+3q5Xb68/P7z84+1z5fL15fzH7tdqjYDlXCfX0pgRFAiA5au3UxjCM7s3qc0MCGgd1PHKhyrG4ODg6qbu6Azh6626dxqTbgDT2qVAoiwiGPbqq2r1q4rgZeytFqjS6lrVw2CjNwb9t4MPKZpv38M8YQcQ4iI8vz6spWLW9vajVFUfdk2RnKqTbfYYZLRjFxltz9Iiq+vL7fz6+5wKGv59NP3pqtTb+bv3/9yHKaXr5/G3W532D08nj5//kwMZv2nn36412Sfnh53u/1+f+i955xba7fbZZp2APb29hajIOI07dx9XVcAQ8SybsMwMOAyz+DWakOGQCRCpW0vL58ZnRzqVqMQA263C4JKEHBwJ0BHUQTSrtZrjBJjmMaRSJoaM+ecmCgGEUBQ1+4cIMaYclbyt8tb63037kz9si7jsAsSt3ku67asKyISQtvWIYb9brdu87ItLDwMOaWUxknyyBQAAaCZEnFCBoAIaqbNVBHuK7jSVdXIAA28qxfXta/XeZ3nusqNQ0gYWYDFickY1m5amqIBuXVyFeF4vd0+nX9Ah930iMi1d0RatxkiuOrb/Nr7kgNEjnncM1LZillj7QhdSCWYce+9pgBRFJsDIydMwojUi2LthsT/y7//MwJk9o50na8vLz9ubV77+Xz7+Vq+LPpSteac2+brcnUHIux966AKRGbWiwOAgXgJgZhVOIUgQ0RrxTxGxhBbEMEWHEQVAjqiWgNtbg0NrKvWPjvU7qtiM+sKW9Ob9ltrzVqnQCFKqWVtN3XrxXpXiQKORDKOU+ZMxplHcn65fFnrW1m2raqhBAWtHQIliYMOzimGIaXozV5f396uX8h1Gqbr27W1stuNrfTD/t3Hj9/Oy7I7PXz88G2zNl/O17fX9x8+/vDzj7VVAtwf9sfjKYR8p1m11s7nc0r5Xv66i8TNYRyGlNLlcmnackivr2cSmXb7Vkpr3dyrLrsc6rYmCdTh9fWZwNpyWec1iTI5AvS6ijALg3ltWx4yEYJ3BCWDFMf9dHKJTYsQAnhtjWJ0QnP1yByFKYzjDsDLemtb//jxI4F++vyFWE77vYhsrW9l1a69NnOYDocwJEEQIO+GAiFNAUV1dm+I2boBdBoHTBlYREQYAZE4AoAD9m5bmY1xyrtS+ly3VpfNasfu7jkmoFthM0Cw1rbSAYzJejfFruauhHR7e/vp9RMwnqZ35NR1a1rKOhdr4q2us1VH98hBJDo2FiQGcGBCRANnbwKoBBgkUAg5xiwBFdq2onlA4H/5f/8dApmBm7S6nG9ftnoptbZqAMo05yDvDr/85t0/WBb/+vaHW3/tzWr1pazLOpde1apDQcDdMIbMAB6ChJQwYCCMEYJoYkiSCAIh914cuvvgjrX6Vkt3W7fbXC/VSod2LyAJkqkXrwYAjozRUZf1vM7rss5ASoIO3mqNjHIv44OHmInofL28bcu2buSGgupgSqfhIecdY1TVWuZtuZW1McpunGqvt9vr8bTLw0AUv/3Fr9193ZYc5fX57Xq7la2+f/+xq769vbp5CulwPGgHM7+3HF9eXrZteXg43U+/hHtUi3vvOcfW6nybhSUEmZdbipEQb8tNgrh6qWuvLRABmtatl4Yivday3ZgQEawrMTIFRHR16929McmQR3NXcwkpTiOhOWLgARHVNEm4LyJ6b8wSx8PjL74j4vVy7dZ34y7m3Ftd14VTSEEyC7Np72BatyUSHg773W4fwxhDGnIEQidmiORIzMiCMZNEYMCQIGZDdrQYMjSb69KxL6Xc5m2u7e36VtuGkRDYCUw6CGrrql77qka1qFaFDr13d9PGAFLNrrfb5fqK3FKiZbnWVta6aS1EYOpdCViQuGtV3RhYQMhEjZqqKzlwVyhKgDwMQ4pDEPHe6ro6QNfOf/qv3rmzqRJw7fV8fStlqa1ahyRpGsan08enw68/Pv6jP/nlP2Ke/vjzD+uiDrDM87yUbau9L2ZlGg7jkKdpR0xEmNIuRQK85IEy36kv7KBqtZXFHRVC71RNa1/ue5TaFgMDEXOz3kKMTlbt1lS1kjooLK3Wsvha59YXMzZQ69hLcyhmndwJOMvBMJ63pW1Na1ezYTgkGMhlfBiSyLyeS1msthgSUdTWKcT3798dHj6U2g6nB6b4x+//y+X6pVcjRHefdgcK/PLlixCTw3jYMQcA3O32ZtZ7BXBi+LueM4GZ3dPOpazbtt2JV2p6PB3Xdd1KGacJAS6XS45RrW/L0sqSM13PF+vKAQ7jsC3ndZ2RmZnHcezq4JgkaK/gigwIlPKOSGqrVVuK2bo3rSlnkTAvKxPepdaupL2pljEf97t3VbeytpRkt9uV1tZtLfOyrUsaZMoDMJZWe6sOvt8f9g9PCqCtAVHIEzJ3AwqBWTwIujq6CuOwh2ngcQQKIBwGMcdlXr6eX78ub2/LtWjv3TcjJetYPDiSQS2Azh5LpW1ba28GWLvXUrV3RYwcS5k/v36uWiTIXK4AOiABy7g7ckxqGwBYJ90M7nXmAq6xK9f7bL62biIcOd7JyqpqYAIGzZ1/8+c8ph0CAAUzWsu2rDftjsoi/cOHX3z37v+yG55SSkGG4/jLWvQ6vxIjuHTty1rU/DhxGEKUKac8jtExanOhirSwrDlLCC7UQiB10qoG1g27UWvFDMyUY0F07WjKTJKCABmQCYGbumMx610BsBZY57otxTuogumt9e26fY2Cu7iLxN6dCaDj29vVUYlVrR72D8IZcRgHgu7LZSWUddmu18s47XbTkSR//vJpWecg4eX56/n8Ok37Dx+/c/Db9bY/HOdtqVvJKZ0eHrqpmY/jNI5DraX3fjqdaiutVZHAzPev3pRS721ZltYaETHTXbXSmpatWlc3A9NxCK23y+vrmOL+dHh5edZtxrYmodY7Yrh31Wttreo4DRyDgakZcRzHnUhEhF633iwEan1z13F3HPI0L/OQk6TdWtYgakvp3abjcZgOjti3RWLcP7yD3ltrayk///R9KdswTuM4Wteyrl2bgzl0DGMaJ4ziUUDuIhOklKwDmDOS9s4hAFLvBbEKIiJFoo52rsvbPF/W5j4Yswi05mu/xhQCB/DoaIjY2roudVnbuq7WWmu1NTBTQAxBStl6rzExB9SuHVxE9tMB0NZ5cVUAb0vrprU07dYNtuatQ+vuTlFCTCmI9d5qdzbSZhUq//af90AD02DGhNxNl+VWa3FFtTWw/8nHP393+lVvvTdvtbVtuZZLa20adwC4bC2GYRqORCIepjhySIDM2E3fOClSUZuZmJBRJKcT87SWVkrXbkDUe299CXdt5NYTS5RJIBmAQGMoIkEluMdea69qzuuyaYPeQNu61tdrf1Ns3muCeJAdB+21rPNWrJlrSkKB4xg+PH3gNkFHUBaKb5db1/btN9/udydAfH19/unT30xTEknu+PDwcRiOqv52fc45123b1s0RUhiQYK7r6fjATDkNd0y0SNSuKSXVdqfM1lrvqWlEKKXM8xxjlCjgQADutm0LQEOrgUmIJcrlckNwgu6tmPV13YhCisM4TNo159ha772TUAgJKaaYe29mPcaUU962zUF3+91W2jzfhnFMMdUGMQawNl+uQx4CwtvLZyLeenVrZSvEvJ+mGAci6bXe1tu8rta6N+29ITmCCfD0+J5iNOhMgBIRkUJwFqRQl9WWK9WVWoFaGJTce2+oKgpxyDEO7GFTVIqcQhwPhOm2Lcy8Gw+EDGgEvau3DpfLc9k20IgetPZW1nEY97tBss9tabU9HB7TsGeAFBMTBYEgvNyW1goimMZmrerSWjdgswCGLBiTjCmyOBKhumov2poW/t3/GEq1KNmbg3dzrb12r2quleu2DEP4+P7XbgRgCu3t+efX+TOKRmZHA1ZCFhgzxyk4GBoKuCEUR++9EipyVSW3CIRDPk35g8h0uZ2Ltq5KXJmhFUBsQgTKLOJgaN2tohkFUUEBxR5MQzf3aqAWgglCM9tgRiIBwKZiaO6talFceunU4yA507Z+DSQPu19Y0zxkRFdoh2FMOHS1UpfPn74npIeH949PHwlxWW5dm5sPu8HV12Udxrwsm3AIUYZpDCmC48PjaZlXkVhrPRz2IchtucUQmbnWeke+qXYAKKUhwjRO7u5uWjfCzqjL7eZVs0iI5AQvn74kQIOWp8ENhSMAEqAIt15yDsTkAMwREQGB+W5gcnMnZjOQEKfd3h1eX5/fv3sy5NfzT/vDQWS8bXM+7ob9/nb92mud57WXPp9fu/bD4bF3Xbfb8eEEwOu2mnUEHFIkFKbIIYqIWvfWCRiEgBg4AjqBo1bs1coG3hyBZOLdXiC2+bbOV1dLwGNOhloNlQcI0HtBhxx3u90kEu+Zve5tWWydATEyu/U27o5Ppw9DiiIeE2ttCHJ6990UDgjCAL0twsgoa1lqX82t6datAwqBsCMghoDDOHIAoWHIQwyOQA6gCPyn/+ab1hyByInJkKD2otYA+lbW3rxuMwVlltfLa6nrsl3m9ikGYAPD1X0FQwSKYSAhAGzatGvXrVtB6AANwYjDYfeY4tFtHOJhtz8xj1/fvqzbZUgDIfeuCP1eeDDrwkGQmJxCdCcUEOrgXFta1w1dxxCOYxgGGcYcJGkHMhlj2tZLa621bdV5a0u1Ngw5IqD1rdSQUs4JQO9I1/ntUi4bEN1uF6bwJ7/6zS+++/tfPz//4fv/b+/rfnd4OB2J6fs/fv/tt99db9eX19df/+Y37kiCTfXhdOpdS9lyyvc4R61FrecUAbC1dienl1YkiKprb4HFwYOEcruW9ZqSCMj5/HI7P/eynQ67siytbKWu67bsx9Mw5hDlfuMHNBFJKTLHu0XUrRORmzUtCHjPYwN4jDlKNLetlBgFVefb7XA8HvcPz2+XrW2nNFrT3npKIYgY+lbWp4eHwHy9XlkiECLfv6HNASSOUYRipBAY2NyAAJEhDA5AQhwzIhOTufbW3d3jQJzB/LJd123rWkQoxqwuZW1rv3ZuUzoMfMjTYRj2QaKbNp8pqDt4pxB27989/eLbX++mk0iQGMdhyCldLq9lXcc8gVkr2ouu67XZjbnUtnXdeuvmThgJABGJHKnHQJRoyrtBArKBI4NIivzf/ZtvgTjFMcU9UQ8J3azWFRBBp1p0K+318mnebs9v3z+//M2qX8BboBZEkTGIhmAIbI7FCjFra/O83V/fwFdzcx0RovBwmH49xHdELsJDeHKgtV5MSVUdTdUkeB6BAgYZMg/IjomjsKAxUVPfimtvjGEv044x370C4RDDjjAGF/C+zbfSbfO51MbCOXIWDCGtrW/tlqeH3k08l2J9K6haah3y8Ks/+fU07L7/4+//8Ld/td+NH95/IEoh7v7LX//nGMbT49Pf/P6//sN//A8R+Ovzy5DS/nTc7Xc///TT4XBkEndAhNo3d48hmKk7TNOkZneCcYxBa3d3JBrCAN3WZVnXJUvc74fX1+evP/9xF3C3O1b1sl7BKgGHGMIYncjACRkA6H7vZ7wni+q2MgMRo1lgcmi11l6VBWNKZTPtmobg4Ld5HcchMNbL3OvdfNUQAJlrq+Z9W+fD7mgO222JIaU8MAcAFeYQ8pCSjCPmnRGRAAG6IUhC6ebgGEAGJQx5L5LQW99uIpl3U1c/325v63XuVSkYiVVd6lawH8aPj/kbxml3mAK7ILe+ofBhGhjamPN33/7y8HAYh3GYUgpDoCFF5Ngvr5/Ol68heC1ba7euS69b0+ZmgPDfAFim9+qCK4dC0u/pfmZKQRABQFCN/9m/+m2iITJkSZFjisjkbghGKQyo+7pty1Juy/k2f1nr2WAL0pG9W2Wq08C7cUo5uLZtq6UD8eCdem9Am6t2o5wPYLyVlSUcdh/Nkrl27YFDb3y5XRggQEAHCZjGvhsHANbmwNmdWSBJKN7UVJtpdwPeBR6gRGIJE2FMcjztvguYwLQ2uK2zAbBTEIwDk4ggMft1vq39dtgdsaUhsGntre1zOByeSq3X63Y9fxr2+3fvv/v86celtPP5upbrt9++n6/rlKf3Tx//j7/+6+Npv9vtjw9Pn3/+nEL67ttvX17f3C2lRCi367Lb761Da/1w3JetgSIR3PH5Qxzatu2mpFrcarktAjCkGEVKmW+XV2E8HY8GvQGN02TeBZiRIzuhm7mbBg4M9N9sBn/HMW+1l1JzGh1gni8hyNPjk3ZtrTnAkIfAcHt7I/NpGoHBASCE0jWnQQjny1zq1mqJQ5r2e3NvrRFiyJGCjMJEwuMAKESiQmqNHSAMLpP1BuwcR1CAXgA6QWQALYUQBcNmWJi+Lte3dT7X5Uu7dcBJphjTuDvFMKAjCwWRXlezW54sDWMej2EIecgxZ2FnTgC4bq+ExgK1rbXV3svl9lzq1t1KWwnRAVTN3GsrquYAHUujjghAjVliDICojuCm6vxn//K7p+O3OSW1KhGZKQwcQoJOYx5Ou0fXdlvn1nyr7CiEaGYkKJGHFIPcjfCYJAqFtdStKxJ31WUtHQDMCCjnfS22lZVZUjiuszUr2nUri3oH5MjjfpyQbEzhDgiJEs0JILCQA3UtDt00LEVrXYWZCClSGo9BjjnuxzwNMUWKrda1Lr16FGHimIUZ1DeU1qGL+TTsOeTujQWDBFSbb6uqGlRhHsf93/7+/3h+eXl8/GaI05iTdr1db99998svX74OY9rtD8N0KnVbluXP/uk/LVu7XG+HwzGlWGtV1WnctdZEZJwO27qGQHnI1tVaj0maVUQH69or9LqszwhmvaTA2qtaCTGEMEkIBBKjECAAEPmdPFe3Wlr5/9NWmMQdCUNKqbRyvV12uymEMM8zOh6Px3VZemulLjFEd1iXtTtSCGZCRCK8rCsCpZy2bdnKel2W3noMQ+sFsee0m8aDoAPnsN+jBCQEEnciYCCAzCJZzZkRoHpTLIuts5lCb/3yatqbdCQnzJu1UvDlckWCKY/daBiGaZiWcrHuIgTg6/JmdI4TOyGg5ZQJojYkwN6Xrb40LSmlIe9bxbLV2uq8XEsrgAKgpm2r2pq1Uq1XIAAk796wkigROrB2MHXA0Drx/+l/+Pbp6btvP/x9prH1GwAEjGNOKXFKu+P0cJgOvdlWmnYstRIkBAGiIafdlERI1Wt1AAIUEkC33tTAGxBQYKSuHVBY8lbWZd2EYynbvNxU3a2RlxCYmDlSGjjykBILM4KEOBp0s2Lam3ktM7MC1G2rCCTiapriKJRjzIEZuwVx9brOVd2EUZhDZI5sVgFxGk7UofUtpR35GFjQWqmFwAig9k0kfP7847zc/uRXv/vw/rv5+vXL81fh8OHDx2UtQfh4PKxlHfejNvv7f+/vg9OPP//ELB8/fty2tawbEu73xxgTgBMLuAH4MOzRSXt31xRCaz2KuCoRvLz83Hu5LZdhGGIeSuuA5Ib73RAk9V5MNYRIyMxhHHZCoZVubndsPyKZ2bouTct+t6u1tdb3+4Oqnd9eQ+AY4zxfkXjZyuF4CjH2Vq2VNGQzndel9fJ2fmulRIm91m27tXXtbUPylBKQxxzSuOOYJO4gD2Yd14ox435v3gGQ8o76ff3Kho4CyOi93dZ1q+Vtu15uL+vtCpRjytVtvmlxpZDQSSQMw1i3Zds2ERGhblZbc16BVw4KAL11Ya3tYnBFXkOknMVNGcmAWXir21aNONTamt9rFLpum7kgRgcCQEdgYgBm5sAROvfagZD/+f/8j3bp6enh2zEf3GHbZvONRY/HpyDHFMYpZ6DxtsyG1w6NwGPgGAIRoTsAiST25M3cuWMjCsLIAUhC9Igy9Y5bm6vdtrpd5/m2ns230i7LejZtrM4syEFBk5AEdg9msasHyYTBvSO59W5WgByF1LCrASi2XnsLIRFAygzWa1l77cuydt9SinkYSUSihJAUQMLOrahrHFOMh0HGMSdEWJe2LGdweH5+MW2/+MWvP7z/xfPLT3/84W9Op/3p4d28zL35fn9EpDzu8jQ+7A/E9OX56+Vy/tUvf1XKNs/zkDMRSEgigkjrthJCCIIoSODgrRRhySm5qRBu23a7XHKSXjtj2B0O69aRYgjee5t2O0IodQOHGCIRE5EbxhyBoPXe1QiRmZC8tq2WupsO7qjdUhqEw+VyAQBAaWoSEgLnmMzMelc0CVxra6UkCfN8u11vIWazrmUFa4DYuta6EtF+/xSGEViQgAy8V/RGOcDpg/Vg3pHJzCgimaN5Jwl5hzKu5ovredmWhiuBcrzV9rYud4cyOLtBytK0rmVTVRaUTM0qeGeEpq69AdRui9nWtQM6CqMYQkH2GKcgudvdqu0OuPUVzdC8Vd82b6b3Qi4TgHdikZgZYt+aajdF/p/+8ne7/B6diKT3ft2+GFzM+xCmcZzIJKUpxHHZytJeMXQ0I7cYArr0wr0hKEcU14AGnUpHiBSZiAAFEYyth6a19blo3dq29mvpW69atpv2DVkAApMkDmSAMrozIBnUbluMwawD8DDFFEbGlCQLhrmUTTuQtD6bVTXr5lVLs66Ka1mttd10TGmf0kCEBt25YVCR6EbdkRMHCr20vm0AvfV+fnuNHD9+/DaG8Xx+e37+8d37b3bTw+vb+XK7HnaPx8NJvcU07fcPP/zw+2768vo8DPl43L+9vR0PJ0Dqvao1BjK3ZZ6FOIfUeyMxb91NU2DT0upati3F+Prla0502p+2pcXEYODq0xhbV3B2UBF287tw20E5igRBQXMwNTV17yKUU+7N3UFEtm1d1y3nIaWhNQ0huOq6LAiAJGrYkbfa1fx42FvVutUYMxJubZOY5O5SVGCCHMckWYA4Zzw99RQdVJkFxZrZ7sB5Qu8Usb2eqa0Ipq3Y9VJLTY/vxv1xVDSzEmVTTunoPCzt0nQjiKZYdAEqGK2WpTsageqttleDpp290p0H07uaCWDsaq01Mx3HHOMuhNDbXWHW7pIaAmhbhWrRCBsCEAoRgnIJCULAECOY1bJ1bdd15j/9l8f97tFVJOStnp/fvgfYhhwdex6T2wSQSRwcuxYAYBJXAyMEBkA1IEiiwpCCCARVq6jmQMDR9c5KE6YQU3ZwIgJ3U7CuCJVBhdnBAF1CAJPWFUBNu7mrdcRObAYuGIIMKeaU0piGIAGB3LRrWbdFDbunrfetLea+1uLUH5+eRBK45QhE3n0l7iEIAtyuCxhkHrBJorCVdV1aZh7zAIal9q2UadxJHG7X87wshPTx47cssqzLMAzn8ysSnh4fnp+fT6fTPM8fP36MIX/9+hxDJAHT1lpDhCmPQ8rzdmVwMAM1JNPeWt3Q7DQdXl+/1jo/PT6U2urWhnECsNaruzv4ti0izMSllJgSEYsICqeUCflOvFmW67Jeh2HIeSql3tcOAL6uGwBIDCnG/eHQW3t5ebnerinFdV3qMmvvgD4d9t10XecYoxCXUpmEiYExBrlHvpkhxWzDxKcTsXi1hp2CUBckd7nziMFeX711DijYt3l9e/7ctjNoc/JGzLhbag1BpiHcltfae7OlwGuDGWBxnhVWh9ZaXZeqDazAcivLUmrRXql1ar2ZeeseeExhCDICgHkhstqLWXNHAUdA7Gq9MuGQEwVQ2ijaMKQcWRBRaV36+Tqv3fibf8yH3QNz6L12X17OnwEvKYK6l8rC+Xq+1FYQe5QqQEEwsJhX1dq1IOIUd4lyROAUijXzjtiaVgbBFhddcxpznlLYJ5mGuKMODJaECYhBAjJAM6pI3dhNodVeSmu69dqg05j3HHLvjYhEgiBFhhhi4hACuHPg9ymd1KhbKF3XthmAhSgp5jwSOKEiVvXFdXNyd9Cq2pQpZU5grZQmEMDMHSRGRxyn/TRMl+vL5fJVhP7eb34XY/r505c87YZxNLO/99vfnl/PJJLzsNvt3j+9+/Tls7DkYei9Q+/rPA8p7cbJoQOYEG/LjdCE0K3fdUDaa7PCIrV3rdX+m3KYYxAmZgGw220eh9F6V9N79Kj1rt1SSnf52pBjq7XWmuIIgMxMRDGmGEOp6/V6LnXTbtM0ufvb63NZb0kQydZlvt6u27aOYyaE+Xp1hyEnBJTIRI4UOYmjgQEHZ8kI6MuNtToTsRBUNOzMer5REM4E7l47UQwPj0T0+vr8Nl+vfWvQQ9rrpsVvxmW+PqPPNTwbXxBb0bfSXwA2a0sp2pdUF1pmva1YVigr1ILLrdSt324lyWEcHq0DoiEpc0WurddSV1N0hOAgiADmqBAahArShYypRSJXL6Vf12Upth++41/9k+y2Rc7z8jbXt6ZbaS+BUorDsq3rct22spULGAAguMUwEP2dIu2uPUxBhsDAtOlitpoqODFCK1s1d4g5TWMe7wcyYP47ij90QXKzTgaMZGbqRIgi2r01nddr39bAIUgY8jDEvBZ17JiUApgqAhIOh/27Y/4GDNRdPdRemy0iuUAHrzkmUAC37q1ZUVe3qtCQY7OtagnhABojCxQF9+PxMUpKOeV9Wubzy+cvw/j4y1/8yeHw9PxyAab3Hz6mKedht85zrf14PMU4vH/37qefflTTx6fH23y15sKsvQ1jVi3ayjgd5uUq5ASmZRMiQa91XZazWUlxup1v7pVAtW2InlI47B9b6yEwOoLhOMWtbPcngLberTvcFw62LiWnSWIu2z2TZ+M03nNTOcfW2vV8ud1mIjocjuM4zssGDuOwIyItxVufz9f5Oi/r0q213oHcgcCRmXPei+QhpZAOHNkRuGvvKvsDkOh69b4G2eM44mXmbiDsQ3QZSMa43w05vV1e5m2trV63a2Mg6sVe1va26uvsz8VnbWsr51q7dugF29JaqddLeXnblq2VTbtqr2bVzLmsrh1TTEzBYQOY3bXWW+kXACIk7ZmskRlUAooeEaVDL2hsnXvj2rW4deQop2/f/yP+h//sSftGUou1rc7CyBQULKTE7LfLMt+WpptqQ9MQxygZ7hQiJBGUFEWGgOScOrd16yTKjOiupblxh5Ri2I9DlsAYiSjikMJY+1ptAammIaYsMZu5WkPoJKReWkWGnGS0O2ZOApL0vrV+VeqMwI4Ujpn33dEhMLL53PFKQZE1UkVWJHTf1vpSvVZTp+5UzG4gW/GtbmWkMXJytehy3O2m4yMiMVFv/OXTjxLkm4/fxjReLrem9en94zjtAARQy7rGcdwfHscpvX75Yl2/+eV35XItaznsd6/PL9N+n1N8Oz8j8W53UF2jEJpf376itxAGIdzevhrAYXda51l7IaFmdZqGVqxs824alnk5nU7d+m1Zc8qEjkBdu6lq7wQ45KHWutzmKBEQ7kB/IcnD2Htf183dELD3tq4LEQxDziGh49tybVvdpeHOMI5BtrIxiZv22oSDkKScmSkMOaddToHGHYq4MPKINKGwQySmbh1yEKtwuwESCjGLudJWZNgd9ofWdW7m3J/1bWu3bXuuuj6vX26Xs7eylnK+Luts1vaguav3rl31vK3b2sEYHAkZKTJTiGza3Q0VHQ1861qbrmYXJ65OCax0qI07Bwyxkc7VoLeCrpINMnQSmJhG4XGKj/yn/+yXqIpkeYqtdQAY84O7bbaEeOrNl/nSuoEhC4W4Yzp2BWIkAiXgEMc0sYVmJaRRMW51hYDobkpdo1MkoSQBAYijGTAF8NGcSn/p3UMYAFKSU0ojIjdwNWX0uzM3xzGJhJ5a897ZG3gvW93cIHFUEjBlEkRGxI5Xp0WtO6ycHF3J4lzPt+3FrXVd1TUEBlocKnAnS9xHAYwQ9uNhlz90Q/ett36+zCyeU5yX0rvGGB4eHpNMt9ttNwVBaa0POcchbMuCTg8fnrT2y9vbaTdtZZ6X+fHpAcm3bX08PUy7/by8aauubT5fRDzFjE7X1x8xUhryeruSakijmZdtwyilbDEGYqq1MHGvBQCCRDNDopQSOizLUkqZpnEYh67OjCICTsu6rdt1GBIjmVpkEWJrddtmtz7td8BCyJd5mVuLSF2rB84c3bB7A4eUYh4GDkLETBKGQSRCSoRsRjgEwE4SUQTSRGHyt5tvVxcnJKsbqFpv5FaXW0NvAKXVxj6vr5/ffnSozKlVXNZ6u/bLtT0/n3uVGE/MEVTRUWGb640gCLAIxjAGycwsLIhiZs7YrXRbl3ZttZhjcTAzcDXXFb0xIrUAPYjEcZjyMFBKLCzJLTCGITKz83//L/8kR2FKEkliCjLGwBLcVN0UELfWagemSSghhBhJJGmpxBpiMAdBJERT6mY5J0df1wvdX0kgIQdw0W6lnt39LvEzBIOld71bExlP2oE5EGbhgYnUTK2VfgbS03QMMKHx2spWl9Z7qb3VDdDcgRlcw1qel/LaDfb73cPDIYS49SbObliaN3ekBtjVDMCJjKgDYZDsNWd+dxofdsNOez+ff77eXrf55lYB+raVlMbT8elw3EfJ18t5zPnh8K5s59bKMO0cOTMzU9O2Xq7omlPsvQSRGGPvBb29f/duma/PXz4d9mMvyzK/HQ5JJK9LWdfPt21llHWZhZg5A9Lb+WU/7dytlHJXqTJLHsaUs5kS836/U1VhiTFebtfL9ZpijFH8fgIA7uLhbSs5ZYkMSEQehIlwWzbr2ksnh4fT3tpmzRD4+nbr3imxdIw5uxuzhBhDkBhjHDLlSQKbq0OAMDGyUjNhBMIUqPVebxCBORjcY0IMqtf55dPnn87rGwYu5p+ur59efs+AQzq4xrL65x/nl9e1a8jpaZBDjlEII6eubV7eVFUiIhELxHzXWFJgEJLAaFBbuWhfzaiqGKAjlFKJELBNIbzf5e8eH757PH572j1OhylHQSZkde5oCmspZ/7zf/vLnE9CwQEoOKLGJDEkglbaravVLq0XIg48EIwxJGLuurhtIsyQamsdK2JAZ1PKeWKwWoqBVjNyEB7M9ba9zuvMEkCplKvq5mBENA0P++GddlVzMyH3nEaEqVYv5WKtmaEDESARVrClWW+t+9z6Br0ihm1Z1nUGgjwcd8PDh8dvH44fEuzNrPYtSCKP1+utth44mHcCJwyAJtKTHBJ9yDHV0i7n5209MwcCrG0Fp/fvv314eq+Gbn65nA/H4f37d18/vX369Ic8DGl/GHO2bXs9v2hvOXCMDAgE6N5vt4tqOez2l8vbPF9TiPtd/PzpM1gBt5R2pa5mnZ3RyVyBXEQQYRhSQEIkVQNzJkYiEkYiiXFblnvQet3WIOF4OpVSn79+XVphwCDYajGHYZhEIiIJxxAFEdTdFIdxV7eeJKD29fZ22E8yTqoM3dbtut6uARzRyL1rM9WYco4pxMTDyMLugWIABESAKfH4wSS05ZUQREZ0wMAumWUi0tbaeluer69f5+elb51DdzyvN3SJIRDSsrafX56v5VIMduPTKR0TCTEisbu3WpopSCBOyEJMTJglDXEa0/4O6SXQdo+/Ye2uqj16GmT6xdPxn3785T96//HXHx+/ez8eplEkALE6b70vpV2X2+Xyus4b/7N/9ZsscRgZiNQdMTBDiIzs29bK4trE1bvWlMKQDm4jAYKDdiVix1BKrbUQcQyjG4NRznumqdTeoQZJzmQOrbZlbgjAFEqttTViD5SEaUxjkNNWb0ZNMHcPwvvD7l2W3Guv/UKxO0LztrbSW3XbunZVFMzbWsxqa+Ym+93DlJ5AB209x91xekAlbSt4W8pW6yZOas4UGIPBFoKlsFdFBqy91AKHwzHEcV22wOHd+29iGl9e3npvKefj4fju/bu3t+f/+rf/2TR9/OWv8268vny+vHzJQzzsphjEVEMgCXQ9n1ur+90+sFwv1xjl4d2prLevX7+EIK6Q4tBqDYyqxnJPNjALXq/nh+MJEJmDEDKzat/W1dxqXQFMCG/Xi5kGDo4Y83g4noD4cr3drm8Ifb87Rknrtrm7IzKxqZtBTENpfV7Xp/ePwxSVfFvLZZsJHV05YBAmoNbWWlbXBgiMEnJOMWVMcQxOTHGvMYG4MRgHopEocSnIpA5EaIgUkqIZYgwxSj7f3n68fL26dhCEPKZfHoZTMCSL7vTl9fVWrlWdkL45PiFYM1VXo9J7RUyRd5FDihTZA0niKVMeKJFQkF0KewNYtptbE+HR0imefjE+/PZ4+OXD6eHhMO1jYvDu17ou6zov5XWeX2/n7Xbt61Ka8r/4198IAVFqYN05pUCMTOwgrXmptRsgoHdAsjGlQGEMQ+RYtqpWHDoZU6+9XqNIzANTBNQYU0oTKlYrEgCAtJspoctuPCry5fa8tes0HWKIjBz5uPbXpb6kEBIfgiQmmqbjbno07gqt+dq8abN1Kbfl0trGGB2hla1X28p1njewtB8fwYWcchyP4+lx/xgDLcvXrZzx7lmuss2KWFJipiHFEZi0ofB+l/cBQ+922E3vHt63bs8vX1KID8eH08PD4fjQ1b///m+2svzud38aduMPP/4QtO2GRIGGnFurVisS1lrmZb5bM5h4uV2nIUw5/vFv/2a9LQ/HJ1N3923bel/mMsfA07Qr68pMr6+vXXuQsK5LEG6t3GvB23JlBG8tB2EO5p6GgSQstarbnUvXSrldLo6Yck4hde3Xde6uzGRqqjYM49evX2/XC7FwjGncr3OZX76W7eIOwzAO4xBSDjk6M6c4piGGJEFYKGY2MIQOaaT9e8w7r96XK3nFEAyZxb1236pvV46IcWfahehpf0zDroO40a1ALfT+3W6Xh23rVfXr2+t13UhS7y1QQLBluylYsbr1EkQGISGLocXggRhd3RszxzikuBccYw7CLI12yCdO7zA85jBmISbCZut6eV2eL9eX6/rp9e3lMt/mbZ4XXUtE3I2Z//xffySOhuqAtYFDZxJAF2ZyL6Wu2y0IpJTQLYcQYogmIVrpS6sruBIS6xyodXuTFESGGGIINI27gWXTTd26dgdjTORDynvktG3nt8vPLGE/fiQIMToLbuXmplM+Rcl3H3Icd+M0qrXWz6gtA3uBebm4+pQOEoYYxYFUeyt9nlczE067aXccnmpdgdYg2MtNl2Vb4FoLWmjNJfZpGIY4pSFT2DOm6MfMKXEIIaLb/HY7X26n0+ndu3dMQkwp5d///r+YtT/51T8Q5p+//nw6Hj+++/jl+RMgkCM5VG3rul7fztMwOpi7WW+369vjaaplPT+fmXgYh6YlDaH2dv/tbth3A3fvpazbaoRWq7q1UlW7qQ7jOAyjqoGjmsecUhyIWVIEpNq29XpLQh/efcjjrvdm7jlmJFSw3isjm3urbZ5vu2nqpS3z0m4zEQb0eZubGpqbG4AahSGPu4eHtDtITDmlPAzpMFI8MierM8ngwwEYJQiA8nXubUXryMHzQASgFWZDa2b19uVrA3j/za9E4fntDDxuZQG7JQnYfevb18vz+TojkZtu22rWb9tb1db6XdRnGDajpo6OZLx2bM3ZiHfTESW6EoGNadyHNGE7JYwA3aE1WLf1Np+vt+Wnl/Jfv5z/+PXlx/nlvM7bMjP4aYiPO3raC//Z//TRgRBFFRTUujssEgV7JRfBgSwSkHAwMmYSzlpgrldFtFrVViMPQoFEqSgIhxwCYxDG8d3+NB1Pt0Vvy1WtC++nvL9vEnqvy6bLuuymUSAxeQwcItZWa5/NNfKJhdU2ZprShJr65RrqNcEWkcbwNKQPwzClPNTea50BsBW8nOetXUyLMbjOkdx6eXv7sm2+blA2cw8AIJJCkJRpGHYhnhgDGnoL2vu6Lcs8k/nx8ZDzqN1VdbefLpcLoB52h23t18slh/F3v/vtX/3Nf0aip9NjXdf5eptrOV+ek4RpTNfz25jT5XLdj6MI3tbFup8eHrrfVGuKQ0pyPt8CRZakarv9Xq3X1pLEtpV71i2EKCEaYohDyuM92tTdm3YHRABm0lZJ7Xp9Xbd1OhyH3aGU0msxt1LVi23L2lq909xUNTBqL72Udb0UL0jSS0O0EAQp0JADICPtdic+HIc4DtM+7XZMoma8O1jceUpm3TmKjC6Dk0FdqBSKUz880u5Azdvb18vb6/ny6fvzy0/Xy9vt9eWyvm24LluKIYfxdntb+nLdztflbKiITthrXW/bum1XVG3W3Btg6b27AdHfZXrcsZt32HbDjig4CiMG3ZhKJgTcLVu/Xpevy+WH8/zTefn+dXmZy7W2FbqhuvchySHzlG23Y/6z//FD660Z9Na8iVFnughZtUiC+7jb8wMbbX3rcDEoDKmB1I6uXRJrLIB9QkJ0QG9eATuYR9yP6XHcvXt/+hhC/nr+spQ14U545EghRFOq9XZbv3YNh91U15kQmcD9tW6vpczagdgJ1RoF3j1M36XIt/OncpkDjNP+Ydw97Md3aYhLf7uU2apR5VZ1W14uy2UXAoXYdW26Fb2u7dJrsGquGwkFpEGOcUwUeiBmbNpq19C0gWuG4XA8YJSyVvf29O4DAb2+vJrC89vX1ss0Hv/hP/zHf/jjH9d1+c1vfuvqX58/vZy/MsjD6WkcsloNMZhbDHw6HepWeqsxppzTtha6r2xMQ5Dj8f2yXB0qM0galq0B8rSfyLm7pXEg4ZRSqZWYJUiYRqKdgeWBtVsvpf5ddTgB4bLN4D4NY2/9cj635Uyq7IhEZav7YRxSqt7VnAOqaW+aYiTmpkAxhSADx5Bjs1brPDDn3SGMQ8w7IKxlUxZ5/IZB0Cs0Rxo0JUlCYVJCvF0Q3NMD7BNznr/+9B8//01z+Ku//Zu/+uH3r/Pr3/z4t394+WsUjaDn29drnS+X1/P6lQIQm2ttZgDCTE7kRq2bGzsquDKEjtE6CoOE2vrMOUpMpSy1zoKb+Arsm9Ot2FvdnpfyuvmKbCQATNGHwKgdHMh5GmE/4hCZ/8k//8a6YidxVK9q1qxaqIw6xGFIhzHHNIyX5bzWM7q2bevVDGjrNURMQcEakFXFrYPh6gBNQ4P+eHwc+UB9kjAt/W1ePqNSwEFEYogSMoC5tXW5EAuAlDY7bUUvQKDNX16/7+067h4CT+4oHh7Gp5wP19v2+uWzxLx793G/34cUq223+VKL3i5NlJAZUEixye3T9b++lT8Ar0Di7g7AAYC3QJjTIwdGnA0qmCpm7M5uSfI4HKzbMt+CBECa1/pyft3WufWax2mcpvfvnrbav3z5/Ktf/RodW21fvnxy93fv30+7iQMzgbuHEA7Hg0hopdTSxnFctxnRHexwPJaqMU4pp61sMaR7pMsMg3CKUY0oxrukuvXe3Za5dgPo0K1JQHRqtfdSWy+IiCHtp2OOg5sBUUrJeu/LMrc3twq9kqsDcIwhDYQMgClkCSGEkHKKceimIRCgp5xzHkAruU55SMOAaM0wTYeuQIg0PHoz2M4oBBJQFQNR+qZCCa8/IjbLgYf9+OG7+eX5h5cfwjD+/POnP3z56aV+X7fX2s5tWy/zeSm317evl9sbiJF1lkZoQxhyzIxEQBIkcQKMIiOHaJ2su0TFYQ6hABTzeluW0gsEM7FKfFt97njTvqkCSUyBY1BQg87cAZv1mjJPO3Exj4H/7F/82q1F9SQSMyjrqpvHSvASOO+Hj4f94zQ+GMjl8rX1rdetVjVHIwNUgi2KW/RurTs0r07cAEubCXyfntatVV2W+mY6R+lE4MCmaA5CSGwOy7LN3WhrN9Ompuu2tl57Kefrl2JLGk6RTwSInvLuXZ6eLrf1dbmOj6dhN8UYbsvtcv5aNt9m095TmqKAK4I4SWi91dJJhMVCgsTRrTGnkI8hjExoXLs1NxEToRR5p8Vul7cxDwC+LGvTGmJCZgmy2x0eHp7mdVa1+7h9Nw3X68vPP/+w3x8/fPjg7inFdduOpwcHlBAR4eX1dcjBzBGotobk025vitrdrBIRE4+7fWuuaiGG1qoMQ4gJKZiBA8WYUx7M6O3t2l2RsDXc1uZqLBxEPKRtqUlCHrhbLaULcozBfKvrFljGcXKEcodWAJoiEwGQOzFHCowIrkQhMcqQp5z3IU1EKUkE7VCVxiMfduYd4kjpEZi225dATHHqfXPMIX1sA9N25q9nbwWjvNuN5+fl8/P5h/nHH19+b71Ogyi28zKfb8ttK9d5a2BIaIBxzHkcU47ESCBCHFIQdCYchjFLtAZWwLlBXKgbujrU3sWMDKszdZCt41prV+u9a7uTh7F162ohMpMSeYiCAZSwAfK//Is/J4iZwXUGccmuXBwNrZvZ0+nb/fhBJA/p47Jez+eviAEwBBkDC4Kpzo6KgTHUbptCtwASAjary1YNrvW6tS+1voK6uzdTc4QGDoBUkQpBr1rmtbTatJs7aYPWmwE1g5fL57Vsh8OHIY/IQ2+qFmR6Ktqbr+NpDBLK7fb29uV67taHVjF6zHGIYUy0H9Mux1Bhqb4AoQS0YIqWd09Dev8Yvp1kX2lR3NwVigBOoFznMg2DMM3r7NpPjw8SkjsSyul0/PHHH3/5i9/elsvL6/M333x0q//5P/+/hfnjh+/GaUopzfOSxyGl8XK5iUhXd/cgtG4bizQz4mhdRXjbZg4EjjHmmEItW4yhlOIAOWbhKByDpBSz5IFCAhbzQELLUqIM6tAd3NHcnJuZbetmroSoVWsr6i3GMOVDkCFNRxkGInGzgN6tamvudr3erGuMCZ1cQUFDIEIgjiGPKaUg4t2ACCQSMcdR02S0YxmjWL99MoghPZnf0KzLBBLg58/6+snn14LukG7z8unlp9fzsyOLoDPUO7SnMfHusH+/G94ddo/74em4f3faPx3Hd2N4Nw3fPu6/yeM+IGeAQODdauul9drBegPT6gsID2kMIREP5tBqnedbXVuvTQ2rIgAKhxDkfgUpRUQH8JQigPNf/F//FbiMMSOpUUO594y0OwCUMe+fDn9CKMyDQTnfPqmqSCKPYF7q2vtKBPdgHpA1X4hgiJM3Xkp5m+dlucy3rwhNKGrjtThJGoeJ2J02tXI6vj8dH8tqrSqBEzFB1A61dfNgxuv2utYXDpzCniyWddvaLUqPTOPxgUPsa7terre3zRpZs7JW4HDY73OY3FqHRUNTUnBHaNVugDzuf7kPh0zMMTQv1d4EG9sOLIlhksSctmWer5fdbi8xr+sWgux3hz9+/4eHp9PhePqP//H/+dvf/Nrd//a//lWv67fffHc6PgaR1tu8LOM49d5DiO4AbiEmNTUAEkGSFLL2Bl5Nt5zuhP4G5AhOxO5OLMIJmSkkIry/t9Tu6owi07Arc+2970/TvC4cE7N4r9qamzGTtu6mRGDkkWIadhxzdyBmMyfEbqZqvfZWS+9lub3Nt1f0CraidVQLTOANrAYilkg5wW4nw4iIPcTAIxMDIvLgYNvlswRmqY4bG1GIPqWX1y/X19eV/GW9nrfruq1zrZwCYyYMyCHKYQjvpundlJ+O6d0Yj8fp8TAedvkp87sc3w/5cTd9POyOj+N+F6TXWuvWu9dmrRmzmy5AM7HkME1pIk9afL6+Lst6nevarBHW3gPDmCVkoqApijCCKROIgGnjv/xf/12trZTmhEAOhA7Obh07Qrfmu/1DCKn1RXGZy1vp5xgiWHSMtRVTTZLBo1MAh95q5jwNj0ihO/ZuWGsQiTH33gDBgAFpGgez0m09nR6m8bDL07vj+3m+Xs4zQmAKDLLV0tTGfAiSurbbdjFriMBIUG/Qt8Pp6dtf/SbRtCl8/vTl8nKNKGY217JsNeWwm5KIddtK6VqMUBtutapBU0yH8R0hVN/QXrHdSGLkR7TspgMPrfbWqghLSG9vS4whRnr++ubI/+B3v/3f/7f/1y++/QgOf/j974PwOIz7cc8SEfF8Od/JuLvdjogQsbUO4EARCYdxcrN128ZxBFdwzWHfu96T7iJBOLJEIHFGRcrjTgGZknDs3d1sGLO23kr74fs/7g73JUYH9N6or1sMhAAi0lpR1Wk8IHLpPaRk0LdtySmKhNp6KbWW2+XyUuZNm4L3db2UtVBXNPXeaq0AyExuziJxdzRCTKMOI5JYWbzPaIh5J1lovjoKhEBogBXCE+f8/PxVuc0V/ve//qv/9MN/KqUyB2Ri5ma9NxQaY44MpqokDByZ2NRqd4kxJFrqYltNGKzrUq61bttWSy8kKsIcLpAqhYloUOu1atl8vp6vy3Jd1to7OoA7M8SBYvI0cBASRAYwMHNrvfG/+8t/A+5v8/m6XMAc2IBu2owwI6jCAtDSMDStpc7X5WvtS0ACz6URmFhDNQo8qluvGDCyZ6GJOAVmBiI1YYk59l7MKwpQ0JQbYDVvx+NeWNBxGpJDaU0dai3dgNRDVxyHnOI45eTQ5/PzXGcHpL65bh9+8Zv3H37FnTawv/rDX51f3yIndeuGAKC9CneHTaRbb9u8qVezQLBPaVBdCVdgrF5bu1Uv5OOUDwCEyEIMHXqtwrG7o0MKYtrmZf3mm+8ul1fVlmP44fsf76BmYXEgRGCR1lqtdRjGECIipRRVNYSoaikPbjYva1c7nE4ORkwiyRFiDIgYYw4xmYJZR8RhGDlkdwQSZDF3Fiql9q7zuvz86XPK6eFwMLPz6xmJ4v+Pp//qlWxLsnQxE1Mt4e5bRMRRKSpLtUI3+17wEgTBF/5c/gQC5AtBkLdJNkF2N6q6qrNEZp5zQm3l7ktMZWZ88NPX3wKIDYTHnmvNYWbDvuHZRNMwl67zON7AvWoAhGDmiEx7KTWlgTy/vrxe386EQEB5330I4ziZqbROIKIC5Ih9jDFGDwgE4NisO+cfIHjtq16fsa0cDZ2nFMxUtWEX6g2l+M6l7T99/vx0XZ/Wp9frU9/aIoshIEjpm3QERdEqKk3LXvO6Vu1qWnPeVW6LE+JUI2DO12V/NstgoCaKOYQeojKp84SMUrQ1zLnWWvY991q1duudEJCIPaWE5JBIPXFvdc+ldahNXG5PgHuV5bK+amQ2cslJz1CUguexXLcLvfxz4MeyXw0qBUAtJqtpQ0mmrnXJtWopAGroINjS1zHdmxkDaHIIhKag2Js0Xp0zhYOxYIfL+uO7+79kibVdpwl/9xffgvLPPz+dt0VxL73sNX0Ykyc6wbzI5acv//gant7HU6AUY2IjI+auAivfae2F0EZBDJxiXdZndvfmXFcwst4g0mRkzMN8CL0vL/kf6mbQeDhMzGEvLx4T+7sujREFxLR2cGRYNutd2fG2nvd9P47D1y8vPkbnqEu+m+9UVUHWfUPA+/uHUsrlcvnNr36dcybicZxKyUzUu6mhD6lJF0BiZw4ie0LsIhRSzlXBvHcIgcmrqveeyLUqYGSmvcqW81YbuLBnKbkxkuNYtM/TVLe1g7Fze+5DGhQMwQjgxqlGCiL7Tz/+PN8d53k+v3xc1+Xh+P7Dh28vy9mQ7h7fkQEzMzOSY0QHYABra9w2rzN46k+f8W5yx9TbKOsbrI2PHxQTeqCSRVdQpEv+8vnzinp3/N2fvv6nt+Wnw+BZ3bbs27JXKIJdtHfL7Akh5F5za7ZrSpAiqTCgSykOcUqOGwfpG1kmEMccAouYdNHmEaxuiwT0FkHA0BA5+rTbamLKAGCgsuY17knYRUOT1mttTWoTIuD/4X//l7Xvr5cvl+uTSWY0AIUmXQVQjZgjm/WupbSttVzrjmAOSTt1VXJMjMIKXqz1ksVjUiDBjZC6KIMji0bcCUrPS3lZyxlYBXKty3KpIqqSTZtzMPv5/eH7YT60nqtKU8j7dhrGaTwE5jic2lIuz1/A5HB8PDx88HFsXX7++vc/ffq7MfIYfBwpnMw7SIjT4WE4PjL41luvjYoZRpcGpvFwOKUYy37Z9qV0py0yacddoKIxSwKhQCSdtGdVUdF938fxsK11HOK2XxDd8XggIjCMyROFcTjmso3D2JpcLuf7+/vD6eH1ejFSH6ICNFUjEAMRiSEBCBoyYRepXeM4MfvWmoEAsJHjENg57yOSq00MTNWuy17Lfnl7JdDDPLy8vjA5ZEAzUfEpCqBD7NKAMPnQTcEAFIipS2UOz0/PP/7zPx6n8P7db0upIsthPtyf7qdhYBd9GMbhlMYJtDkyjonYc4wp3jP7Do2k1uevYt3fP6qhXHb0kd3pxsdG68TU1v3T559+ej7XsW99+ePPv1/bZZVt35feltzQjKXWbkgcrJPUAlqk59b32opAMcx7ftu2Jy6r1tJhR4dmqJDNiSD1uot07xKCaFWGA/tBlV3j3qXUwqBMLAjBAzkCkxtTIOeiyl3qtmeRxP/T/+GHXLYssu0bW3UMCCgdq3ZANNDgmJBUkJkNBVS0dVBiHqqC9wOgMxVmZSa87WSYGDQEIHNNBc0DaYOOTLXmXmVbulbx/vG6t58//XQ9X5hlnobT8X6cH0530zhMy3JB4uQDmsyHY+CR0Bn2bT0D8jCdUjzGIea+/Pjxv14uPw4D+8HHyaYZfTBH/u7x++hPoOgQ6pZNUBTn+T66I0qMnKKfVWNepZSqUBWdoKFDMs8WEgXqJtBVdds3FRvGYRhD67nXNk1jDKG3Pk8HRL67u1u3lZlR4XK9Mrtvvv1OgfdcHTOzq01v00JVbdKYGZFiHMlxbcY+hDiqETtHwAaM3nsfiD2zb9Vqbd5H6bYs19b26/V8nA/s+On5K7swTlMtOe+Lc4SIvVQmM1Am8swAtufsvI8hAkCr9fPHP+Rlebg/TMdDGk4NkTzHIc2HI8aR/EA+cEpunnk4xPE0TkefjkCh1tpVGBRzhoZ+mjixMZI/AB4NXe8K2+JCIoV/+PTHf/r0N9v2ooAZ2rI/WVuK7DUzWiIXgp+di47Z1MzYAKz71okwEDnHPKSJMSEQRTKiJt1AjFiVS91aK8zBUSScvJ+IYq9Yc20td+kECGRp4BTdEMkzDNGJWG9yS2WtWXpD/rf/0/vS1nVbEA3BwEiJDRDQAYQuoKYpphtMAQyH4FLyvVfHLuGAQkBYsYIIe+eYydvd3WFMvremPZeqKhRSYAZTcYjH8BDtzuN3KXwT/XG57M9vL4Yl+HiYfnh4vJvSMPqxS13yOqXTPIwpBeeTCpReiJA9uRRPh/um+157rq2WqpbjwM6bQx3GMB0fT6f3Q0jMvonkuobgGIJ0cJywBTJCSain2iDvrXYDZGZh89yiU2J1qrTntdYCAPN0GKdh3S69txjSMAyIDICt9cN8TDF8+fpJREzVhzjNs0sTOLfuOyEz+d5EBcwghABmMXhG9s4bsHPhxvYldr2rIXqfyA0KzC6pYRO5cWiW9dKbni+vZhpCvCznZd3mw73z6Xq55n2Z5zn4MW/FQFQV0QXiYYoqVms1s8tlZaJp9H0vap1DCMPRhwBoRkZIGMZpmjhw8PF0934+PcQ0pWlutXfCmAbHxCBea28Xc47v3ncSW17ICdGInKituDbG8HV/+vj207JcgiPUGDh6uooBQjIz5yH45L1HQEJWw964i6gVIkJAcubdOPDUVYWoknYQIegGrXZpak0JiCGxm408gGtN+35WMW2izZyzeIAhBccUAgHC7egwJ3aJiAE7/+qvj73ttRUxZWa90UTNIQQM2Ayv+176AreHFBWkxcG5QCrINgV/imkYp0DcRbfDFB8eHg7zKRFKbzVn1GBMzBydC4Gjd17iyb2/T99r84M/DjRv6ytCPozvE39zd3c3DQHJRHHLa5N8nI9DSBxc7a3VrCQUYRrH6XiHZnVXQucobWtubUseQyCRxuExhYkIAhPU5lCjT4Rp3XPNHUysV1WuDYkcUDdzYOzBDTAMNAzuQBBrbaANEVKKjvDl5Snn/XR6cC6FOKc07jkHH+9Oj1+fP5eSh2H0McQ4jIfjbYa1LOuQkmO/7xkAAQiRiDEG7z11aSZASCLdh6iGop2YnU/sA7MPKaqamZHjfV9K2XPr63JFgC5933cwOh7vWi9aiqmmMAwhtJLBBAUc+xASMol2s35+fe2tb8tymOdpmkTNBZ9SDLekR3JMpLWD5hSICZhdcGzWRAv6oGpSi2e2MCAbaTNkGkYA5/Yn216svNI4Yppgf3t+fT7n6/Pl58v6KsIsU+IJnQntBojoQwhm4IISMxMzkgkq7EiVHTkPIcQUJ2ZXVHO30nLXSuiZSFsDIQICA4IY/MmMuoJq7eWiRcBUag6Jw2w+GAAwsw/BuSmE5LybxtE7j4B8/5sgWpvchD0qKIEPOAGTQlWwEEeTUPbuMUQ3hHh0LnmX3h3fffftn//mL/7Vn/3ud9883g1jUGieeRhSr5Wgd2k918EPXckQ2aOx+eCdBKeYpoP3p0CjDyx+Vy+n6d27++8Ph2+T51Jya2VdX9a8hmma42zIChlBkZTYvA/e+zGdTF2+riaQ83Zd30zNY4oGDRwTFlOVjrIc5/GQHsFzB3e9tsv5TaGrshp1UXZ+HqJjZ+K883MaPU4IhFZNbri9slwu+7bEIaXhoMD3D98sy7ps12+//fbr15fL5ZKGdLx72HMep3kaj6romFutx3l2jkR6jKG1uucVEWprzrkUh3XbACGlAQilAwfPzqsSMdyMGLlkUemtLesGyLXWknPOG6LlLXs/RE+Xy0cWDT54Zu2NoDOaivZWybkhTdfzq/QdBFpt3lNrfZjHYRi9854xMHrvmVF7dyZ138q2AwJ7f6PTRPZhfOd42q+vVks4PloaSUh7Adw4jn36FUFtXz9ePn9iZDH4/PT248cfP37+by/L5doHMRdd7I7ULmJXHxJTAlTnzXE0oOCcC0YkagqgzvngD4GHjip2C4wpCuLYI6KaSFP0TM6RDWTBTAFEtJRSVaojQajjFNLsmRsxkwNTjT4d5nkIfgjJu1BrdihcSxcQdmbqzahr09QiOGn67btv/+Vf/q/ev//gvStlX5cFBj2M0WGtNb9/98P33/2O2L1ezvGPYa/ly6efrL4RYnGacw5A0TrBWDtt22YBj3FMPqrUKm9pnJZtqXoOhEyHwOM4PBJGAi9917awrL3KlvfTEBEcIYzzY+yHLlcgq/Wy+xlxZO+0tq4QcHDF1+xdHPHYOl+FAvcYkGIIQxyk46IWMzfpL+dytDWE4kIk4N7ABWSfEFiaw8AEhoTgnIGU2tAsRq8CJbfj/eFyff769Pmb9+9+/PjHZdmm6eDjtK47cwghtSYACI6lNxFhtBAdgKpVM7led2Zmjkiszgk6Y4fkxBqZK7khApqFiUT0lrrbe48x9i6lFDFCiOty/fLly+9+8xc5523Z5/sxpNhEpGdEGXzotZVWWsvjEGvZr29fT9OdtUqJx8OkAs6Dc14VuykZqBixBR9ZUDoARlFEFR6PLr1TMYphfPzhev7RXc/+cCdz4qxaMpRO3un0L+N3D69/93//n/8v/6fuD0D2+e3LdV1aKx22ujeQQ2dBGNn/7H233jAz04CowRnW6RYn3rW2ut34CQJNGqmpWhMRIC61ExpS4IDsiZkZRlTsVqxbU0GGODAbjtNI0WNgdoFdbGK9d/JEzs3DaR7uhmE6HJ7dcXqoejGTrWwInmlovfXrVoh/+PbwL379r//6u3/zV3/164d39y9vy2V9Ou9PZV3Za4bK3mHfHc2m3tT10td9z2Jm2PqOu9zFQ4wMKtVqkZ0oFGggDlrb2qUbbHVvuOR2QTTnUUTavtWYCB1jPE6P/Pzj9fLKJHOKh/Gdp0cT7XrZ2meDfe9PzAeLoL274KPdDeisWsvarLae2RkrJj9iRaUtRj8fmcg5luVyLVsB7D6gg0lNW4EwkifWjsaAyIiRWUWr9x6NatsjAYIu57fLdn7//r2Iff36PExpnAdR6GLzjVUmLabxslyBEBG7Kvuw76uqqmop7e506E3fyhs5donZpd67KrQmdLPoIItAzuUGQhMRAFzXNZcu3fZan19excDIlmUxJWbXVS6vz4ch9JozOzZp2pzVrx//5Dy12nVQ56jWOs6TIxZpiELkkJiQDAWRmwqnMLlo6JhpHqeG8LScvbOh7/Hx+zF9By9ne1vs0cN0wDZJuXAcm59p+ubDn//rf/jj0//1P/6/q33yB73oJeual4w2mm1AcNnWSj1xCxG8zWgGVh2PzqEIK3rfkjQD9WCkqt1AWist166MwaNDVCLHySEKIhLdCCStSQfiFMijJ4NxShQSOHZIYqy1ADRFdn58fPerDw+/NgmJf83/5n/7F44SYVDU2jYRcBasQ+7ZM//6u99+ePjm4f5diH4tl257177u+3m5Xt6uZS8CcFn2L1/f/vDHH798+nHPWxMpBXu1qD45tg6X9bLpnkHIeULWiglGaZdWzw1L6RuKlWV3GIZ4h9qUMqF4GgF5Xc/7frVb04SP0/jBx4FIS3ttuJPTVrVK63Uh6p6dA+edB09ACEwAoSjsriujWvUOfPJE4J1Lwak0pIK0eUL2jt0At71Sn1Jgh45hNOlddwWQDoAYghfV6+Xivb8/PZ7PedvK4TghMFMMYdBbWFXX67YpWhoHE1PVmAYTqLUtyzYOh2EYz+c3QAXjGFKKqbeuaszsfWDmWz8t55xzvn37VuVyWVVp3V6enj+2ku9Op5JLq9msOCFp+3Z+GrxDgPXtNZDWchnDUGodp6m3hmCn0x2xy6WEQGIopSFIFwNATw7JIXoXJgLPAX1M3s/oEmi3JtabeYhxYjGEisAWPXlCZfQCjIgAaTil+PPzp7/7+W+W8nPp5ZyXt7fz1rdNLue3t68vX9JwOpzeOcbgucvNhPqBYULqBq1XNkBkZecMqfe6r7mWXLt2ucGGYZcKKOS8c56IS5e91K3sBIIOCMY0DTGdnH9wPDI4U5O+GUiMDl28O3w/hvu87a1lR+w5sEpzECoWU0Ficq6rfn66/N1/+9spjBXbw8Nxr+vL27VIVWvXdf/88e8altOf7kujr1/Pz29PtWzTeJymiABWuwdBtLL3q/SqxYDrzhRYavfYj2TdcTEgdR0MLZTr/vb6VcqUYfzu4WH2Cae70+nhsq/WaqZ4mDHEiOjUdgDobTd1oCCtdtmJXMdasTiKSIWYOCRjV5qU3DerNjrUzsoucEiYgT0n7KZgWbJnYwKRQhREtYE6Am1NoVYxaV16Z9R930IQUhhCfHu75Fzn5PN1D+loxGZS9x0IXy9fhjT+8O13vbUlX6c0pHFo0ve9mOFhGkreHBGbB4Dee2ut946IIhJj7L231p1zOWczY2YRWbcrUjety7Z3KSE66bbky908Q5fmUEtF1So1hKTaARwaiAijy3s/nh7Ldi55I0y9XZelMiUQBeuIFdQrMhj5OY44HsaBE1WQ3vsU5jT5bd2ROry86SZ4jDiwbM3WDecJD7OZma5IiF2Wry9ocDq++/j8saqtu+ZaffPX18v58vpw/P59+pcDEPhPzEtvF+VmOnV4RzDd+FIRBrWuKL12KbVK3Vo3M2zXa8HsPTOXhnfosEVlkZbbWrTp7sXE+SMbTV19q0VVCTvI1mQRu5bmq25/+GiAS6C0SXOtbp48OdRuIIk4eA7B4Ui+9f4Pf/r46eP/mQK7mVNKh/k0jXfDRNLqeV1zeb3wuW98fl4W2IZhQDYDGVIMKfS6AyiAefWtg3NsWJpSXQ2pjPdhIphcWM1ftcEYpbbz8x9b+4aCb4O6BxpC/HD33fp6fiofBbhXUd3NtMvSFaW71ldnAdXVqvv+yuxdcB2kd3AAwYERThTApa5WautaY0QxFOuCFaOGYRARK016a3Z2nqvhpldqHRgHSkzgrBkIgqmYoG7rMg8jGa7r2fm4rqtLAxHVtm9LnV16/vT1sq3zryYR2a7bdbm49+9zzmXP1+t1mqYmwt6jSJXuEJ3zIqpqIt17bwbLsgKAmfXeb8q4tYaIvfe3y2vvHYlFdLu+jUNMKWmTu3G+5K11UYQYfU0uhMAcWushRVDTbin48/ktF43egXOYarOu6pio1x5jDCH0Zd+sBX8f3AHZI5TartoToClE0wz1i5Y78rNzTgZUqeCSYnK1mp370/UPP//p67qXvee177lvuzXAHXMuW+D5z3/4q2N6x33x43GzM7rVOyZ57ZkAJ+ec956hC1BtDQAr761vva+m6NCpYa6FvAs1RMPmyER7y3sprW6dpejo48ok5qJzDpwubVFshqvZuu/Iruz1a9u+3J9+zYz8Z/9uFG1ohBqkGzOkYQwhBudjGhHd8+vbT19+/vjx08ePn3PO0+Q9A1swH6p29RRCEuwOmREGj9G5ropEHIN4MGZVJsYhRsfRuURM5JEjDiHGOCoRMGoHEO2oxsopDiEehjGFBBDWfXu9Pt0a1Qbdu46Y12WV7FGBzWsVRvYcxvH+cPwuDXdKGBnGGBMMg48uRgHsNZe9rWVtuVbpSuaIGdAIGbBbQwZAExUzQXKBx+AYW8cmvRXqTSU3BezmHOZSCCCXTIzeh2Gc95JbqYr4/PrinBtS2ve9lHJdrjfu7Pl8LqXEGIm4taqqIhpjmue5915KuSWrruuac0ZEANj3XURuD4CqXi7XdVnUtOa9ldxr/fDufXRO2n6Y5327MEMKYRwiQnfee05IEgMzEdiNKhRqvV5fv7ZyNWwEAEa1bNv1DUEcg95CiAE8OQbn05C7bKU7RHKEUKBXZ2RVFc2N3mLU7sgNZCDPr//4+3/8289/eL5+rdv+8vZ8vVbt7Ch4ckz+tz/87sP9ffQuuNB0v+YnwGamiAxGIopIzA7RI2KXrqKt1rpnaaoC2q13brvJDmSmoiYCvVnvLdfahG9+PFT2gFyNt9Ivez8bFKKCbIDQ2l7XBTUxjMzgan/1NoEdtAUGBi0ijcNgZtp1mqYY4+F6+Pr16fJ2fv78/Ge/eR80xjCUhi2baKUQ5sNkabVqwdSjVY+7Ztci8kgAYeQEiqZArAjdKWlzDjTMxSFxDUaSAAgBoDOUtp3reizHdxRnSu9PD1+fpy/LV3AcEwzhEcyCEzf5AN/03tU171JIxzQegOJer0xgm5qSRyDn1rr3lksp1/NbrZWI/DAeTvPk0FSJaG9aS3dBAsXoRs+hF901+8BBRY1AuNZNrCIL89CbtX4mDiHNDsC0r8tbyfLw8HDezkWyUySz8/ntZuZprb28vJRSVFVEzteL956ZxdR7f9M5qhpCyDlfLhdmNgAAWNf15i1FxJzz9XplZkfYexcR712MkVTZWdkvIbFK0m4EnimIArASh21vrr+EEFI8IpKPiYKUeoW1auvBNeeoky7rudTtNEwpPZhxbc2lgDw6gr2u58s5jXEaRgonEAVXiJ1koRTZz1ZW23Jb6trl+WX76Z/+VPs2pON1bwFwSBGJ7+/vDocjohnmp31/vfzUwzIcaGQw2tiPAKTKxIMHNFRiQC2QuwkyOiaoItqMO7IAkDarAYFT7L2boo8xBHRcMJg4zSYkVblw1EQh8tgbK7bgSG0a4pTS2EtwDgysEUoIYEX20lWyJx7CgT2LaowhhMf5cPf8/HVZnr5++nyKwd3F0dPsx/O5K3MI0EmD96FR3iQ3Iu8BPHZSaMSJCElbjFEM9l5VtXTotUJrzAzoGluLQGRTGM1sKde9jY2xd2Wm45DeqvX+DEBEh0j3/nBsXR3OyB2RTWg6jNN0341iUbHp5XpmNGQx28F2R7pJ3fIupTmPBr35bby7c86vJruUct0qVZr86TBNw8jgoXoplkWZzIVgcgRZxXYkkQYiEAOjSs3l3d392/n5MJ/Kvsp61VqHw6nW+qeffvzu2++HkGouS70iYozpcrmSo8PhcD6fD4dDLuV8uRyPRwAotYpIl5s9Ku9E67qmlGqtt3qg1jqM42V5ATIjiDE5F/L61urK0h7ePZZS0MAAFeM0phSobfVtW8YxUkCRK/OU/Ijzfc5RFcAEsDBPczyICBF2oCKaCON0CNMJwpgid9WlbtbJlGnwoEKOMSWNo7EXnFykt3/6h3/8+VPDaQynWuTT+ac08HEeei2q/f27d48Pd6BmJJfl+uPXn5btHI+I7EPw5jLwV+feaWNVBGBVMQFtYAgEAFWAKQbnzUwBHHnng0d2jEjsI2lGUnMgtwJUhJBrF0fBsXp0jJTGZAKllNoBsDkvcUwO1LdmAntwRIwEKK3nrQQXpekt6DM4H8M4z/Nyva/Xr8u1pKRjGN+ffuX5+rJdSaTmUFQOfgCXPDvHENnC2JlJqymoYBVdCdyQuIaqKo2MEQWdcwFVtYGRmlk3Xdf1/PIajwQhZDZ/On5/+LCVJcYBqZK/BDxQJ7ONvUPrqkABhF1vfF1en5+/tpaDSWMXnYteW88A2/E0lAJjYucoefVRCJBq9riRSqupAcCo0VJKswvUMvdObJVAnHPrVoIE70JHcMERg1qZD3etVzQIIby9vdRSxICZn56eHPsYByOULl+evt6f7oZh3Pf94d0DEU3TNA7T29vbrcd/E/0iUkq55Wzf4uZF5PYY7PtuZrdBGAEys3OulL2VVbqCZ++jKZOKqoQQwdjHMaAntN7A+9G0lL2mlEJ4N6acmwlUJFMA6wZo3ocwnHxIcZyQHZELMeaemfkwn2ovvXdGZ54RGYgtOkTPwICBxvk//u3f/unzW6VVh3UoLFZENdd2OBzu72b2rve+bPnLp09tKckdZNFitBzUD0oe2BmSmGYDj4Rmpr1Ql0BszpuZIAnJbZwa0hAC+QAC1tUQUYEACTyTsWS/sxAN0QaW1gk4ACcNw5TiUbS13q/7xzh0Z1XyBsBUXLVuiDCk4NEBgCNe1zWkgBFjZDI7zkc3Hw2aNBMCR/5ueiwZ8rXtuyDp/Hifhqn1ReCsbrUggcYE1illdk/rT9BLcofozTCDEWNwbgZQGihusO91NeVOIvgRramEOHSqfpq8/5D67IIZ11261Gc0SvFUiie31Vr3l0DB12Zvz9ty3X1hIdBIGmukYtwMwjU7PyFpCy7GgB3ZO46AMVgasFzgbVmnOdzP3xIl5xHYc0VsSCzQ2mAD4UG7OS9qtbU+DINBLfv5ML03gdZaMx2Pp1o6Gt6fHl5fX7/77pvc2/l8nqbplhcSnHfs3eAvl4tzDhHNrJQyDMPr6yszhxD2fQ8hIOL1enXOMfO+70CQ980TN8XADtTW83mM7GkKae6917JrLeM4DMOw7zlJ6NLCNATkLpKGA8XqPLIlJp8OrqqYdAK7JXqQi4/3j6f7b+Jp5hCG6W4vPe8bd3XDGDFQNa1Gh8E8A3k0gJZFf4K1p/Hxh9/+y//bf/k/fn7+2zjwIQ2lyflyzqX98MN3hEaAHfDp6Wk9rxPEENLea7tCVxnV+dh4uoYIAkELSXemRETOupqqYzVjs+5M0dB14zKk+0S2tb2rWOudLGAKyCyYMxrxYfS4d/DdxSgd1rW0ICmM45QUkojl/OKErDRua0upBB4VUFxPYwwujOMIDp+evmzuejwehykNHIXZeidCcZhzTilFC2+ZCEfvBdAZeUOVuhc8e5EOHaLj3h3BEEOG5+7eUozI4IGcJzIzeKhdXF09XBVrF8jkSz231zn4KUzD4NGTH8Ij+pUDgSBAf3n+qpeXcfBOw7peS8ult9aJdCJMA5wc+yH4ooVZ5ylQ8Hip2xbEqHdXGXcFBEbygedxqOJ02eStbIdyhTAosWdyzgWCUgqgTeOpm+br2qyh9eAcAaJZiiE4XLdFRSIFj7ScLw8Pj2/r5oJX1S1v3vve+y1GUlURIJcCQCLSWrutj4lIrTWEcPs7zNxqXZZlnudSSs553Vc0NUMiJ2BdxTMP01BzDxzytpZ9Y2YXvHOOEK6XF1TNCncPd4yEaqP3AIBINJyc1wGw1k6AcUgA4Bjj4TAcTmm+I5+ks2pzzoFZa3U6nDhYUzF0PIwwRgPCJlTq60+ff//8pmC//dVv35ZP+3JubdPSrpfy/tu78RjFJAKX5bJf68zHaWARObj4uu+XXc38dFDildgc3pGDXnalzccO3YlIEa1gYKaI4jGT3Tmm3pt0aEICDHVk9q4ngw4cYJjG6LrZVnqAXhWdAIAMomnZN2EXvGfkwL/9VzE3Q1RrHcETo4jM8zDGxH44HA85b5ftWmUtrXagwCF4vOZrVeutbHuulGtb9/XrcBgOh3cpzLWVt+vHt8vbnq2phh6whVyLqQBUduxcjAJiTI0cpg6u9JaaOGoceo0q1rVupWxFeq2CKqbITM4F7NPoTg/zn5+Of72e1/P5qdelCy1XzWvr4lTajHbv0iGNXbpxVdyBq3cj26gWe2skGDm4OHYFUTBmcRoGnQ8UCPZazAWProMiogFC7wyKTL0VsxZddEwApN1iGFOIZV8cKyM6hLKtYp0C7jV/8823rdbt9RWhzfMhhPT6+pbGdDyeSs7eu1JK732e52VZRKzW5pwH03VdiUgF9rzdOqHPz8+9NTPoLZsZgoq0++N9ChGpidWS9+BcCnGc5lKLgayXq5Ya2awXlVzragqOA0fmQOhSCMGnqI7Y4TDP82Eejg/kRwUWkee3l+vyhpDZMRIzknORXSDndB6EA0Aw9WyIvf1/f/83/+nv/2Ypl8C89XLZlrfL+W68/+2vvovBxnHeSv7y+lwLTcMxetfFqkHGstUdWWLwjEjcKRqgEKCBbr202rppI8wN6qbKJogRw4lHhyi5qQGYOGxxJB9oxAHdGKfJW/Kdndq+w7ZBXVVLcxC6YCvw+rqc37a8NP7tv5kV0CfmQGqCINMUx+gfH+8R2fHheDqKrHtd961cr28qu3OUSzsvL+fr83W9TtPh4eHekA/j429/9dfH+cO+l/P16evXn99eejmHumUphgVbKyK4ydSr68X7PEEhaXitZd92FCOHKMGgmzYxUEJlq116wWy11h5xHtJ8d/ztzN/96t1v//q3/zsXTp++/vGyfOnN79fBSIgc03RwLtHkICitjVsWcIjBfIzJx0HAlAAMSpXeSQHZa0ow+jDFQdTEOHAigmjsQCN5510ttdY6xMTM27L2XAOjZ9i2C0gDMavdJ1ZtwxhCcGOKj/fv93Xdy3UYhrvjO1F4vby8f/fBDHtvt7YPIUrv+74DEjP3XltrZkZE+77dLo3ee62l94rWRZppRbN5St4Zgs3TdD2/HQ9Hz+yYCanVhmBgnVFbLWMczCDvOYQYh5G8B3bROx98cOyir71hbckP4+EU0yAKCmqiy/K279cUp7vjIxKiZwvBnAdm8JEx4Xa+fP7T18vl4/P5P//T3359+rlBV5Dc+hDcb3/1/TByCKDVPn98Xq7FYSBP6MLWa4OMTNoBzbMHZkMqzhkjErIZSbPasogHbdC2misWnimN3if77zQt7cgCHskTpCjoyAcmZAGqKrlra3l5UxY/TEChd73uZbnUy1dZL5X/6t/fcWBkDiEAqkEeB49WQsL5cNe7m+cp+LitRSTXejm/rLW21vvb+uVyeR3GcYrvD+n+V9//1cPhw938OM5TKdv1suz5qhrzqy5b3pcmewUkB9Ah7ovlBZKOhAm6K+Jqt9rVG8VwcjxuS9OuhOrIiLg3JUZPzrLuucdw98O3f/7u7rtvHv/iN7/+t999+5dvb8vLy09Nt1bJlEzAgUSXmEKjvknNvdXegw+RxhQndASA0iTnWjOw+MTpEI4ePPnBaAQNBOA6MXIMIfnYcjNp8zAC4Pny4tm9f/8Qg9/zCtZD4H1bY3TTPBOpYzeN0+PdY2Batyu7Hpwf4+H19TkmnqfD5Xrh6Lp2qc3Mtm1DxGFIYLqta9c+pIGIPn/+DGCttd4bopVSGFWkqcqcRs/cezmOc0CWXu7m6eXpyTuqtSHi8Tj3llE7oXnn2QfnvHMO2VEI3jlEY3YI9gtqwDSENB6PZpprN8AQomjd9gXVz3d33hF4tuMJQ7TakRjJgSx/+zf/+f/5t//pDx9//4fnf77sl63WPcM0+h++m8YB0+jN5MvX148fv+bCQM7QhFAAimYxRXaI2K0gVGJCAucEoKkAmuvNpFAQIzOA4DuNYCExE4GYgXUQCsCezZF5BzAQYAT0tWNXEHGR/Uhp9hRi7lWq7ldb36wVL9L5z/+HcQiYkmNGpA7U02iHO5/by5DuPd9JzcRYSlnXN2nW1cq+b+uec12XThC+efer6Kf7+dvffP+7u8M3Zrzty9enf+5yTWk6v+5PT3W5tFquYxxO/ugsdKGaVwHSNKC/Z3dC8H3vCZMie/fgaM6tGUByo0MGy6o2+GMMc97a8/OXOI3vv//t+/vDPLj5cH+YvmuyndcfazbrWmtxkKKnkLzzc6l1LzkXbeocOaZBAbVn6U2as+69QuDR02gdS1VpfnQjexfd5IckbDVXqn1wbt2W8/ltnqf7+1OInpm7dMcEAMOYjqcjmkdET/50PDiyXjNq0b5EYrS+79dxjIQmvZupiV0uV2autapqSmldVxEptSBgrbcV77VLYWIA6FLR7MaPYERTG+OYAmrfrZfz29da13EYt22b58NhHvZthdamIZHzIsqevAsi5pmHEG6BzoiU4jDFAb3vzI6JfSB2BNy7qZj3zoEL3rkxKEXCiGPCMLStEBaiw8uX1//8d//l7z/947Jvdc9Yy+EwfPs4zZOkFBXx6fnt45fz8+tadjRAIrjtxgkYoDKb495VQAbCgRk9E7OJ9Fa7tg5ZpXViSnEM7BzdPHEEpYMqsrJDIiTv0RPp6A1cb5org7qR/Yx+8ki0tVpbL2uTN9ZMWXq2nX/3bw+GYqiA4hzE5Dkop0rU95wH/1BLL/lqhqYIioqt15r3pnUCCZfLMkzh/cM3kdO7x+8Pp/vrcv7y/PPT298PyQ5j6sDrNUvWIdIh2GH0oxurwVuzbd8NwKf7NDw6N8q+VDkLJlNicsg+pdPd/H4aJ0eK4ATMNIAO5/Pb1/NPpbfDcJ+G+bxetryKmmgu8lYzdkUmPyXnCZwLHIY171vJpmRqBtRE97JL7a0hAnofmLxIb9V6A6mdOHII3vuirffeS6beai457/M8P9w9OEIyM1EENADn/PF4+iXCxyCGdJgm7dV0R8vUNwLa80KEoNRFSqkEvlSptbRe121lx4R0G/1K74jYWmut7XkREWI00NZaq4WJAAAZvYfREeqet0stm/QCIIw0ThOSmfU0DFoFSL1zvVfpPaUk0k3Fe29E7Lxzkcgj0TzfueAQiDiQj7nUbd1VkciGSGGYmCKEaA7UEOLgorPL2/r5/JyXt/NyOZd2XvL2Mk/0/jGloN4FhWHZlrfz5fXcW6W+S3QhhsguVSuIjikh7TE4HwLagEjC3UDN0MxyWVtpVnHbdlFFUkTyPsTgnXU0VeuGDVw3kA4oBiDUe821kmc/MEcjFgLcZdtLXa59f6v5rSMLTls6kJNCxrRr50gJJXrwAZo0D17k+vz2D86OtSpgvLt/ZNfX582UerOau3OEhP/4j/+Q4jH++XxeXomo1O28fGnW7qcpIn373stS3qAGF9nQMM+uScUnxSV31nJ3hyGMCKRh+Xr+Gr2QiHURkYM7nPwdefPAREvukHMGM/D49nr+//zH//nrz5/++i/++u70WOpivb2/+9ax/HN9uW6b89JaNT/nWv10+vbhL+Xp98QyhVTbYuakAVEcR+61mUEjqL33XMAUiUSuag2lETB1GgiNzaf4kAZmJohDsMv5lQhccLXXENMwHsCo9itiQ0CV1aw5UMeAHPdaHYsAnN+eptMd+7GVWkGX66VJvQH+l+W6LGuMsbVGxMtyLWVnZpHW//uHnAfALjtrAADDLTiPcQSTy/mplZ2Bj8djqTsTzNOdu+NtP3cVZu61lH11Yey9ni/PY78jIu9iU1HEVMU7nu/uMA7LUn/x4QU3pgG1kDlzIxkAVEijtIYircLf//zPf/+nf76cn7ztYYD30/04eUIhq9bgbXt729/2Vjk5KsaoLhHHQA65W6+9mVEgdJ5IOIox74VF6pgS4+hwHD2VgQ4ADJ6dkEnfu3YVjSH6yNitGgmxBxcEHMYhhOCcmwcMnoYweJdU4VTrZdm2UPSo8Gvvg4bJMAXnWsDa0HztndnYgBjNnJGRudfrp4CLD+8AGaU3hcBO/ZyxiIo1ORyj9/THP/2ePYXBXa9r0XxdX0WzSAI/B8z304R3Tosgzq32Bc9DdN8i/r7YBfzvwrv3D98R4PO2rtdj3fLVem1CkT3FzHU+zp6PTvU0WEd8W5oLnghaq/+///of/ss//T++OX149/40zUPwOh8ejx+sPWHUFa0B0BCGJja407fv/mrdPwdvjiOTC2RNzaMvpLlsu/YQiECxNGMTbt1qAfGSJg4gBMyEDpoREIKuaxZT5/zlcubgT6fTPI9PTy95PT8+vG9t1X4ewkgUiFwMx6HXouX1uta6cKZoVqA3g7xcxbpjzDmt1633DnAopQBAKaWUYiBmtzKgi0gMQ5WaQjgdjolkjkYIl/OllMIcjE16v15eVbtH0tooxJEOvZXIRES1dxeUiKT15foGaB4ojoPnZNAJo4iwWRwHM7sul5zX6IcxTCaCDOpY0AVRVzar+uXj8z/+4cf/8J/+X0/bP0S/pWMa2CsU1X5+tX15LYSV0dR5ppTYHB/v5uN8CEzSXW/mAo+ng09BtQd2ApZrmWK4v5uDHwkO3gZEjH5ACb3lbX9e3l5bL6JwOByGYWit7fuuquSd40TBD8N0nOYYiBF9nNh5U1bVfVusau1FiQnRIVACd58GVIdbvaxYydethaF7gAISnSfUa34OQoojIuYMDBZCmGfPfmtFCXQaR7H+zz//PaicDu+A83n7qeV14006YeWIfJeGHqyqXTcT1QnNs0RnOMzvj9//6tt/gaR9WS4v/wT1pXfJyyp7QP9eOr9d1vF081CeybsoHPAOAABgJUlEQVQQad33EH0c2fn549PHv//yH78+D7/69Xfv7n4DHO/mexJbzyu4XPorg/cJc88+vJuckb45502Lj6GXak188uobtr1bIRc9QHfawHSxvVdyqft0wMlhAtIb2IwI95od8/n1DQh/9d0PZvr1y+fz+Ry4p8j73sc0DuFERH5wYmRNS61GMZemKnlfi+61iRVppiWU8/l1vW4pjZfL5VYS3IxDoo0IiOg2GO6SmfHx/vH93YFFQNvr26c1Xz+8ew9yX/Zr21e0Fj2Tad0XHkcXgveht8rROmhpNfokVZpmFIWa34fvuyg+DsfHh23Zrm9vAlFq6b0675xLDMlYoFz58A34SeqKLy9fPz39/Prysl651YkOcRiIukm3DrX5bas9uzCmcTqYdilo0yF6fjg+nA5TcJEBHac4TvM8ppQ6kPVeW0bgKQ6HaQohOOdDCCnEmIIp9ayvl9fz8rauF3Y4zbP30Tpv27buCzHM4zTE5IMb0uzQdVVgY6eIiMrcoZSy9K33HiB5N6dJ3fsxORhGLv2tr6KmllfI2BWw0Bp9kko5PynG1pEAh0DTYXaj8Kq2V/bAEe7GE6fjWrfnj/8NYGWogFikRH5+dPdZGliMRFKvJE5pWGoPQR8OPL5/fPftN+/mR6n7S5hQiMScyfvDnfBgSkM4daitSRgGrRK0HEiKknUVV5Kn7+5PL9Bent8MfgKgIR2D42NqujnsQTHn/eMQP3R0XeucHnNrHV5iYpC59ada3rw7fZiHjqFcw+t1WayThBBcNS27TsErh+nwcHKpl20Ig/dhub5V6cREHn74/reBp9//w98oqGM8He/QKPoYY0QmciGEYGZKEIfgU/QQnl/fntclV9tqbYsAixtYr7Ws2SOsdQcGdJWAAUWso/lbPDyQMceH093pcAociMqyXmqxeT6awXw8AXa1QsCJPJkSIptGC8CgKmboiGvLVsrxeAzVer2ufHFPdv/w696rqsYYe1cyRvolrtOQ3OCJk3nQMAM98MAVP/38+vL5vN2Nh3/9L/7ddXsVKQbCfiD0JUt53JHIMVsrvfdmGngY42GcUgqxlOZ8OBwOh2meh9E7RjUkQ3DI3gdGRBUAlJRSCCGlxMxtLMNwdxh530dmfzweQwgAePPSdpEQ/Xizptfau5gRALjgmJmRzYyzc8XVWhFxGCgNk2OUoPlhkh389UsDCs4Y0AOJWbluhXo0ELWqgkYkODqyITpPgRMroXn+9ts/+7Pf/CvA/s8//umf//h327aH6IMymNtDJwzDcfLkZVXpebkWDDiw+TkNEzisUmRZ5Ok1n9/2hGgMLdbTwz1AMNTDeNzwqtqdBSM8HN0we1FY2nWXqwofh4MzbNvl5en53WNQ1x2GYZjKuXtH5PpaPiMJmld/N6Vf7RuW+paSeYnQo1kZhpNPR4kmxuVtMeBAMXgICQiH6KIh1FoDeUV4vbxR0+PxaK3evb/37P7wx39Yt+Wbbx5TCD5w7z3FOYbJeYcMKaXWunE3chHoeMQm2gnlvNfcl7IBWdmaiBDgntdSaxwHExIw4kjACKgqCOwdD0Oc51la+3L+4hicc2EYPeHl8hScc5yCn8S6IDCDQAnqCQ3ZDTQhT7tc0ABIFOz+/v3z29dIFgilLwlPfb+AnyaPS8lEgyAjiJSiw4jDjIFEhGGH2lRoOt2dGgWfhmEYrmOXPM/z48P7eb6rpS1v51IrOq69XS7n3nvwPjoGJutCBgIqveSC0nJAP4wxxdEInEP673bXm99pHEcRWZbldgcej8dpmloT51wI8bYykVJyjm/bRbeJu6qFELz3jj0xAoB2894753wIhBhTcs65zTq67mgNYVCFXFr06nwcYGQfNqlbAVUUU0B0jMVU7RrAB+eIhV1N0/Gv/vzf/vWf/5tAdpof397e/vS6GLroHBjLxsP9dPBTaCjD8elFr+uuowKb82TYtuX1K//05Xr+p8//7eOXP3443IUh1b7u/Do/xK1fqKJhN82m1Xn/8PBhSqc914+vH8Gah75tbyEkUSj5vF7hcDoQ4RgjHxN19UxSr1WfU5p6nk/zd8dxfr7+XS4rycm6jVMbUprm02u+krEjr4AB4vE4taRt6958b6poBLRue4zj4WHWvBOxGf788Q/n69Ph8HB3924O4e36JXedxntEDiEgk4h671VBjJghxjjPcwerXWutPvC6bZfLxfsYo29dzdCUwSgOoyKMmAAA1dCAQMdx9I7Ken19+RziMM9HYthyRXAdjdgUiMk3AzZA7V3yXiC5kZ1HgHGemgTVbqLN9PsffntZLxDG6XA/H+8tjPtum8LaXXDk48BCDGYE6J2FkepmeMUOHeeU4OEgz5dFiiWfprvH+/v7x4d3x+NdKeXVR8/OBb+3vu7FREAFWHvv27LmvLXWxPp6Oe9rRrM4TMfjcRjiOCVCdzOHH4/H4/GY0rDvu9luZjEONwN571lEc85E5L277UuISK0t5/3mn73xbBBx3zIxMrMPjoimeSRkACAi93pVPQ3W1h2uccTrE1ofEwpw8DbchYldXlsBRDM0laK1K6qxxdW5atbv5++++/6HD4/fW13uh4d3pw+f/B973XsaO5MH9AhAtlhtLS/bvudGnTYpE0mb19e3z69r+XL98pr/xCMtUlJzPo25IG67+lx7I6K+XwHLw927aOMhTDEOX69PrRZH7fHDSbT2SnnPhtda2QaOfJdGb92aaK/yenlG/+n7b7+pZfeR7u9//fL6iRhQjiarKZfN0FzO67K9DGlyGAMnF9yIzvlxdMfYAQHfnQbvYFmvY0x1a1++fnIe3j1+H2Mcx6RdVOE2w2Jy3ge4rZwQmjAjgOebzPAlpzGGXFKJTar0SkRgg4iIIBkNwzROM3mHiNo6IjIRqsbkpTWTQoCgmPOWIjuE47vvg4N9P7vgTRu7UPc6eTXfL+evSHfjOJsyAMaQzHAaRrXCYfrh/beBw/HhEQ4jdvZec0eAJmXzU5rShEiGgxkZAjmwXUQ8uQFcqaqmxTHEMKY0IOKyXnPZa621ttPkYgzjYT6JqAgzueB77fu+b9uWc6611Fq3bbvNAQGAGMyamDAzOwwhhBCYKQR/M1M550Qk53w77r03ZtYbOwmh9y6tI2KM3jlnpjlnQ1BVj56I/O3Dzsy6KDO7ZWsVIA5TxWs8tKmEclUS5yM7io64T5aXAkBmCGb9Nonvrfc1jWYajvM37959OE7HhdvT+atjOB7D+bybWOPuAwFoGGJNsPZ95UvxLtrQetqvFqb+Ol7Q1fX8Y9uf/IRE0mTz/ujH2ZBI6/X6Mfkxm5yXZxU++gepq7IZdD+gM3COAIW9xOnQZe+6G46r5ISgaHttpWAtw/XpOvjX++lRawc/TMO7fbmSOZOom1+gW8bI7+6Hd8fheIpucBExWoiMbgD2FGOarV9fXz6OIZSCT8+v4zE+PnxXlxpCV6u1dEAijOM0I5OISKvO0Th469B6RzK1hghElMIwjnp+W8wEkRC5ttxacxycc3FIh8MhpaQi27YhueAZVUWbiF0ulxgH76fpOCD0prn3Dh0P8X6Fr94hGyFEQ1Ixaf369hbIsZvYeSYzswZ9CMEcOece3r+HcRYQlk4EJJlLl1LWXtLjoz8kUoW2ElV1ASWUZb9sby1na00VQkjzPDO7PefnpxfRTkQhhOAdOsZeb+H18zCf5gP8MlTd+m0UaFZKeXs7l5IRKUaHZKZoIKWU5+fnWyr4zRYOoLXm3rtqF9GbRrpNS2KMagCA4zghAiKaCQD13pmc9z7G4L0fYrotXtdazQBCcNsuy9qO985Pc6DycC9Xy2WP2+rCKZlnh0fcDXUH7GqUXOrUVKHk0KUdjvzbb//sm/lbAnzbtp+e/7HKl4f7u7e3S94K6wGnBJiQePKx3g+N7o6QoDC2DGlYZQi5M7yW7ZVYwImws96EOjIgondE2pb8nI1ydz99et7X8nAcI1MNFIcxOk/UA3iF2/qsihjBsbcjmneCBxcOx+F+6Pt4wTrKSoFmbuKBQ58FwLiOPCoQxDa+NzWXODpWZNCKa2vYMjHyEGrLZbki+pz7ZXkd5ng8HdctE2CIU+34ev3U2vV0/JDS2Fq+hQHP82hCqk16RyJUQ5AxsKoNLkzzeLks297YqUirNWOym+9/KxkRrXWH1E1qU2udXd9zdiE83N+bEiiUXAhgv3yZ5zkN96ijHxgZJbfA3jlEiLXsOXcfW/SOCUDUByKku4djGA/LVsYUKXghFhMA8uhi4tKvvW6Bjzj6hsqGslWvUUC23OuuzBxj8t6P42Rmy7Lkfb9pD+k5563XnPdapTvnT6dT+/DdOEUz6b20XmKMMQzDMLDzJe/7nlUV7FYDQN7r5by8vDyN4zyOY0rhVhjczOG3CwEARMh7T0QAeJP4zOy9IyJV673/ArtGcM4BYGu1lFJrJWJEdC8viyMPTR4ewpScMvEBvtQ1Z4PTPAwH11KK+Pq2AzSkgFa8d2ahNVfy67v3w/3xZF3O9enjx0+fX34s5aPnyXnb1x3MG7pv7j6EkGDEb5OPPi0tB/OSJYOjFLpUkcLBBz8jJe1tt/bSn6DxAe9y3bppxY77cKJ78d2MQR6890OIIY0AZpoBgL0LwRE5aAiVlL0aDtPMGkrrez7zeJTKmiVEF21wYJhstdLEOwsp+JDSMCXnnBar0pvkXbeyrdIV/aE1a7VySPtWyrYwFGLfG5mCcwwUAfp5fb0/zC56BUFEVem9m4CZ7ftea44xogGBRKaCxtSS48fHe316W9cVoROB9Np6abWv67perkxERD6EUkogPhwDk47zvKxvKQxqZKZpSG09A5YtvwaKESdzAGFj8yF4Az8Mo0g16L3u0+Ho4mAm1SDncv/dsVUgCACuSFnOSxUNAkCSEqE26gD39+BYa8XLy/Xt+a2VXLt0EFVEALDWKsDNrQzMxMximNes1rdcETFGPZ9fcl3m8YCIXSoAxBgJVwBC4pzz+fyWcwk+DWMEMGavmkVE9dJ7XRa6rYaO4xhjjDE658wsRhfjcEuVFREziDH+snEqUusvEz0irLf05dbKnm+lcynZFa1NUNHoBe8PofuctcxHLlqQafbvGsHFvW3nDXoNsVUO7HKkBIKAamRf3l7+9PqTM/7y84/Pnz5rOCdffNB9lwZbLvL54uP07fvhDjmUob32fSkrGxLDFJCD1L2ywaCOvM8Ng0CWjRUZIrA7wv0J0fng3RzGaY6nBAM4MDJqqmAKGCIzeSjKSInHkLCDRA4xDIJt3/fsAcA8hW5dBRybJ0J0CQxLJ7GAHJNnM+7CxGrQBVSVidAxkDE6Hubr8iZViJk0sY0E1LUypzC4nz/9kxmO44N0vUVEbvsCarVm7WzWWt+cB4BIxMAEJM5hay1va0xcW9auZnDbA2N0iIxEZnJL45umKRL1kn2gt9enQHI6zK2JtmKKQCaV1rrzzGItWIhxaq0U6WGKzrGp62rSpbTuB0pxEEql6eXzj4cPvxYeuO6xr4t16lV7NYPkZk7Y9Yz0yPSeQi+wf7z8+Onz5+d9A2VmDCEx876vALCXjEyGsOXdkJxHI6DBDWmeQ7LW9mXblv2miIZhIHS9FxEjIlUlAkQw6EjM5GM8pJRqzdu2LcsiItM0ee//lwXREMJtWmImzA4Iu2otrdbivUckAEDE3tu+3xSX6Q381LuZtVKdc84Iuwo2f+4iksMolMinYC2rFOlZGyNQztm0q8bO2Xk2qgT27uH+bjher5//+Om/kMSfvvy+d9u2vtKL91Fd7+1sMH151oiRJfAA3Ziyw2ohHsZ4OMZD8A55ByuJI3PMsepMSEYuOJ6ccUBP4FiBvRuGaRoP3vvS276vRWswm+c5hHC9Xve9rLXmsMfkiZlGRMUQfErx5i5m57jVnHMXAUTQoipdwDnsAH3ZSymAGoJ3zqneoKqUUiIiQld7Cc7zYZbOE/ngOW/73d3d3d3dH//0T1+/vh4OB+9jawIAhHi5XKYhMWIpNThqLoE5NfXeL1IAu3NumMLLW2t7Y6Qq3QE6YkIzR/d3j8FRLftNKw8h3VhOptuyvH3z7ptSZLk8DTGt6xXJIYkjR6ilnJ0/gVHyoYHekCopzV6UEvXet21L4zQwQEgxzEwOAls/wEB3TsrazHZjioeji04DooIZAY10ehen82avr5evZc0xxnePj85Za8bMzoUYWRFciMGPkwvssECPQ0rsW60QXK2VmYdhCO6mW4jZQghEdDgcut6g1thaSyFO09RaS2m8yf1bV7T3Xkq7qcTbEd+2LaR4ezZKab02IkrBzfM8zsfD4bDtZV0uqspMQKja931vraz74lA8ks+tCJKu7o7cOFnH7t2IwFlKcNP3D+8P/+5/TUSBfFNkgo6dHD/cH+4f5sMYacvnazUZT+NvWiHH7d3x/oc5tmwxBE8+eje4Kfmxa7v/8A4Bkk/H+TSkyTkHCq01h3SLbzJDg16ll2ImED07DF2KiKBBbwVMTASlJ8fjON7WxkurRbuanq+X1FJKIcV4W7NCROfcvu/YGiEwO1WV3kW11AwGjvkG4Fz2xcxij94FAGpNalPRkoYoWtQkpEGabxqM5bKtj6c5pPjffv83y/o2j+/HIThHYAEhni+v23ZhasGhd3dNhdkjOVSTbiaAyI45hRjDcDnvrQMx+BjikMbhOMYwhCENznncts2RU6Cucl1e8nYeY4xxeH27HKeJwLq0+7s756HsNcSxlL3WOk2TmFK31muuVWsNQxrGmRsrWN93H0KIQxhmCKOcTqRMe2rd86DOVfZIfoaQwJNiJGQwczFRTMe7BwC4Xt5ccPEGEFEl9Mzu5sNBx8Q2uBDJH4CMSTy5wENzsbYQQgrpv5/g256Ti85z8ER0a2gi4k3Bm8Hh4L33KSXvfSnler0i/QJdvYkcBbvFsSEiETjnrLVKYIiH0zwcT8euLd9v28aOzOTmNOm1SesOu0lvMTlGRbJsWYuehochPd4dHh/m707j/d04E9Gt8nBIAJC7iEEKPibnBu7aHkb77uGHt8fvt/0vwxA88eCSAHJH77lbHobxMB0BwMBFJqeKDCH4ECKYa61VqaJNW6+1I3oB2bC31hwDWTciZDDotUttpipENKTBJc55u16va14VzHk+ptl7/7+c/tu7X1VvRkt2zjGLyLZtWy1ENA+jmTl0xkDeASJ630xNuqGhQzFrKo69DwnUWlVjv+WVUHOtnz9/aX2Zj4f9WmMnM5ynoZTy8ePHEB0QN9WmAsg+eAAQ63ZrWKip6m1CNExTvS6goCBpGO7uHw+HuUtdlirSFDiEWJZt3S6tCaM/HU8m6r0j56SV+/v76Py6nYMbQpqbSO16HzwCCHQ1QUSRtl0riI7zgQAUwRKRFOsFPCEactThmPxoWs3Q2AF7AwJggm7Woez721mlTMmFx/vTNN9+a6115znGiIigpl3qtldrS6DkQ9+7Ic8PcxqG6KOQIwMk0t5LKURkQNueJahXvXV7buugN2CemoUQYkrH0yl4fwMriYiY9N7XdV3X9Xq9vr6+qqpzbhji8XgMh2kckxt8N+17RkTv3GGexLSU3cxEmo/uzt87D9qtKkAcPScDD4+n7749/Prd3Xd3p4fDdJzCiO7WSIohxhgCR2cdWZBIFdQMcm8SSq/5wZHpezICdoLaJTP4lIJLHMh7DkROTNG0SxURI+LgnXNOKJlvreVcBHYzdOQG8I5YVa0bBxfpxk1o+753afM8u+BLrtZ6b01VgdB7fxgnZr6d/sv16pyLMarZrU2xlixym0SajyGlZISAaOxMBRCJ2PlI5MCEGW++XAAgdABUa2YHrdVu1TNct0WspTTuW21aQjh6DrXlsi7DkMZx9IG9C3uut5sq59zarf9grRUAaLnVmn1wt99rTMHF1M2uy0rUDLGUxswqYL3fwu7naXLkbwrt7XJ+PB2ZXGud0cWY9i030Rj9XuvoI6cA0pExuoCIRBSdJ++QHbqYXILoYUw9d9gubjgYB/QDQCS4fRQNAQgBX56evvz8cd222krNzaCWupuhczwM4zRNtxeNqoo2LIYQtqofv3y02n+tv0nvAgRA1a56S4xG70Ct1spESASI+75fLpdt21T11gCd70/z/WmYp5tMIiLnnCem6FprwzDM83w7nLd/bkppGIZhHm6F8rquy+s1Oo+OnXNdGwCodp/iLwdvOsaEXqi7hOOYHu8+fHf83TeH7z/cPYzzADdsG0oz072JtRgeAECtIQMGkgKtFIKmZtGHFKL2XvZs1kF7dAERXIA5jWSgKoyQIpemRiyqgCiqHtE511oLIZLjaZpUJefcndE4iUirklttrZWyizZTZXImtpw3IPPOTdNkTKXVcbyZ9nJKyXmvZreb8XZ0eu/X61VExnk6HA7zPDtiMd1aaSC3iTpz8C4GPxAREvwvprTbmB0AvPetcvDJtC/rJ1RTiap6PN7N87H3Xtb1MI2UkmgDNURkJunVINwoVyKK6MkH3bJn11rbS3XE3o3z8T7E6bpspdk8z4j29PQEQA8Pd1NMvajz2nredzgcjm9vr0PyQ0zX1zd24fH+2HJda/YRvE+3MzTPx81Wa+aHERHRsU8xpCi9HXkKh9nuH8EffZxUTGVHbWDJENgE7ZZBYYgI5gP769v5x6+fY0ye+MvnT9fL0zgfT6eTiG7bFkJg5iHEMSYiZECigb79TWttHI/IvouYisiNPEfOu1prNzOAYRjGcbzVtTcknpkJ2DAMx+ORvXu9nOuepXczSyl5vL3TjJkfHx/fvXv3S+enq2iP5AC0bXlZttLq7SXovTeTFCIhzmm4/bj79//+f5RqCmBcVOtdev/N46/n+egcA0MnVa0O2BSbmRZZVuRFa2sQ3RBmBKemYwoDJ0U1aTUX60V6j+xFDQGk9ZwLAyAZcUDDwIHIPHtV8e4XEjIiqEr0kZnNTNUINYQAALVlW2Df19fX19rL6XS6lUetdOcJALppa80xGejem4mKKqk6d+sHq4gQ0ThNv5kmQfXex5BuzYT1utTe0jAdj8fj4TikCZER6HYdEyI5IqLeRMUI0BRMUQT2bd/3PfmAzM65w/yQhknLdivyWqkG1FoJwcVhNIFaOhF5H/atCPZbx9oxhxAuW0GAIY3BT96NCqAkSH5Zz8+vL9JUVfH+bhx8bbhty3GY1uXsnT7czQTWe+u9E93FGJf9XLc9Bg4cGrkmfZqmy+VSS/PBsWDNOxl4H310eH+vw0PvBp747r3VInLxdQcHwg4QGRAAANAQhvnh+z/7y+nhXlp+e/oKqAL+9fX1+fn5xvRNKQFAjD6EgErjENI4sBujd6Ve1q/PXXFKQ4xRVJGAhXPOIuIt1JKlt1KKd+77775j5tZ7KWUcRzTY1+3t+WVZlhszBm/pMGa3UNrbW+nWFTVQ7S1bRbTWRHslMISO5qG3Wsp6frsdhpvKcv/jv/rfcOem+vnt03U9z2EYgmdScVKkoKKqNhEfAwBsee29ooGZBR28ljFBmPw8nnwMpay1KVMEstoKAYIpKJjZdVkIMaWIHlpungyYVOU2CNz3zM5574kJAFrvqppL2bbNzJznEEJK43zUvVTY9zTN4+HQchGvZrL3mmuRrg5oz8U5h0x7LVvJt0efmYEpjoOLAU1/gc6CtdaWZbm9uoY4j+kwptPtDumtllJUOhA6ZBHZ1nXbdyZELdJaLV1EhnSK7FKcxnE4HQ4qrTft1q/X693xtO8m0G/Cwzlfar3d4MNAufXcdiQbJz/v83Wv0uFwOADyy9ul6032oGM/j8dlWVSt96oBylaGMKjqvl2mIURmMDmd7l5ennvvhIxkvRStzaKrUnJe7+7ubmUlkyMkIgI0gtat+/Geh29UUFWQV/ZIneTtTP7Kpwfj8YafAGNAwOn0/V8df0Dr5+f/+p//w7kccObnj89PX760Vt69e8fg9n2v56yqd+l09/hw2dYtr45RxJblEkL43a//ejwelryWXkIIZBBdnIJ++rRuWwaAw+FwQ+QSAoJt69pbQyJEHKfhBsjY81byfqugeu8hBFAr+4aILkQCy3XvrQEAIEbk6CMTAaj2dnk7b9uGTCklRXDffPiVFNnzutfSqjFDk6oFXEdmjwaIaIRtF0IzMyu1A4m0ubeIaMmZUim7QYPeCZBjDCGI9N47iHVUE4N1lW6OIyKSc0gIAK3126DuRkEbp8OtzL9V96W0mtu+70h293AXPA0xHOcDM950WZwG61Jr33ttUh1RdN4ImzRQM/llnnozkNzuR1UteS+lgBr7kGvbS2bmGKMjDj6O45jSWGtWMFUDQEZW1WW5tlYd07Zc8n5lVPYuWIzOnQ4zKJjpul0R0f8ycQQRrbUNKQG4VgGDllLikHpvXeQmr81EsbjA4zSD4jQNW7Mvn7/msuxrTyn9i7/+qw8fPvSm27Z6arXYHGiaxlqrKqSQapEU/TgOtWzbnr2PMaVpcJoFHVTteV9LCkigomkamZk9hyFFF0cM0hvggnz0OEBfZa3XL+faVkeWti0+3tNwZ8AIgKCIBGgG5E7vf/Uv/+3Lup3P/zy68YcP3xHRMKQQQhvKuq6lFPBYa77p4R2AvTPDdV2/vjy/Z9zX67ZtvyDAiHYXBW4OH7h1/W9P7O2l7py7uULQtOeCzK21vG17KQAwz7P3/Etpq41yBrKS8/lyuYkl5xytMA5zjFFAh2m82UtvvSMnbVNQAEk+PB7vDbpiU+utUSntdrmwc6a/tJNJpEq3LjY078IQIjZpXLQl54OPv1QqqlpLV1V3iyskMtUYIxiZoYq1squojwmDQwMVQcc38dd7JwK15iId44GZneNtvW7b1ntnB2zWttwBeu9ZpMtNX4GAtdJ67x7IHJnorbT4755B36SXulntaNS2vQNIU+cI0AzV++h9pFv1pyBSe1cg6LWpqnNeTJDZAEyxSiVwwzjVmnttZsZ4GgY/Dr6Utams+waKyQ9GUOriKZrWlsW5IKpompzXKhkYrCNImmYX2Gorua619qqXt6+n4+Hdu/el1byvKNU/JPYOhHr9RUEhYutgWlMaa2kKxkTjeOQAajaG2VBbV2ZGVRFLh6Std9Hjh/f9/oEOB2kg61MKEdyMvf3h0x9rW391eMBuyDCESckjAigadQSHoAB0ePfbX3/3aXu+bFxu/2PBOWYG0MO4q8hayrZdiWg+3jN557G1crm+7WUX0bvDHRs1qWq9Zm1eg/POhX3f397eLpfrDRZ2Oh1778uy/vzpo6rmvJnJ7YpoRVNKh8Oh9/7y8rLnFeCXUPGb+Cm13shi27J3qdO0TdMhOn97FZJIa62puJwzACDB3d2B+U6g915FbNu2WyECAEy0l3L7IzMyUu99WZbTfHDEaRjYO8/Ox8DB3zD2vXcDMTQABETvHCIy4HVf9q1seW+lHg6HKfq2blLbMAy3HuuteEdE2KCBxBiJAAla7a13FYkpEcJW8m1jEJm898mHqgqEztgx+uAIEAMisZo0A0Wote9l6606/GUC45kcqZkh0eFwOBymEBwihhB6761J7+LjbdxoRNibIOKYprJdu+kUXc4XBgSD6OOcwjyn8/VlXfcP999Y3/d6ueOjNGn7BilOw7CXgkwxJShVRZyPQZpz+61KVyEwj8gs7IfBzL4+Pd8YEGkYgESauhilNxcJEUM8pJgu17foAbAri+aShtSk+3HQ2ok9ExH53pvz1KTWvR6Px+C4WgvHb9R98BBBnq5//OymrZMiuLL2s8uWQszSt+wOyQyREA0VjYwAgUAfvv3h+Mc/JG0559aLi46ZWu4AMIwjhoBEKcYQvBqYGRYKtYRhKK303gTBkIMP0+hDiLc2zuVyucn6m2szhBBC3Lb95eXler2s+xa9K3uNMfrojsc5DU6kfX36/OnTp9ra8Xj89a9+dZzG6OJxmkMIwLRtm1kSkW1bMrkhDPM8xzjEYVJVB4SMFIK7Oa0RDQBak+u61FpvRO/bYH/fVwDzPqUQM9Hlcvn68jyOowve3yolZet4a3TcynOHKKpgJgCttWvON4VTa972jT21t75fl+g8gO51H0MMMZrhvu/blm/SiAi6tFybiMDNSNV7axWZMDiQTmAhDRFJRJjxxgUkAASu0gF89KoGRG4e0g29H8boXSilXC6XUor3YZ7n28TXeb59hdtGRUrJRFOSmxOrdyWz4KMzkLK5CIgYfPzw+M472rZlXa6mzmGo7Xw8HYBwWV97WyO9NzMLYd13Ju/ZdUFkcxiji4Pf121HLDlbCjE4LwbIQGC3io0YAWRIEyu01ud5IiKOad2XvSxpOPR9Q+QUuOxLSg8mRkS5r0MYmRMBijQiAhXpJYZE7AUqSgFk//it0piXp77tby8vOW8yTw7Jj+H2dke8vcmYrBkymCFwmB721rflNaW0tbquF+cckZum+fHdB1C7LufWmkivvTl2I46ttLLn1WBIIxHH4O9O98F7hF/Mz8fj8Rd05M1jZOqcO51O79+/X9e1tXqj28/zPB8nRERAU41hSMN0uX7p/e0wzyG4x7vH0+kUQthrORwOtebX19eXlxci16IowPF4nKYDEbkb5+zWZAUARAYAAJnnmZlzzq+vr7VWIrgxKxP7IcQmHT0/n99yKT99/nQ8HFJKtzbWreeIeEveZBAVsNb7uq6qOo1jSuk2676cz/pmzBxPcV2W67LEEI6nk4i8vb3t+87MpewGWspeSjMxBqzB4+2rIxKRqRkCqHhENgBG5wjJvE/MXvddpJnZkIa7wx2iNRUAQITW5YKoCocTPTw8HA4HQBMRNck511a89///ot5kR5Msz+6782TjN/oUHpGRmTV2dTe7wYYIaCVIgEBAAClxQQmQ9Dx6A+kBtNFeq15LpJpkc6huVWZlRlZmjD58k81256uFRbV8F4A74B6f2R3O+Z3zz7KMYjKlRAiOKRGK9Ry01hQC6wKAUDKKQqrrEqJonIsJJh+oUAEYgIFg1Tx56zQCwBlLGAUAWGth8pnKFwM1hEgQLjJl7DCPw2RiQiHG6JyFIHgfx3FYAODbXU4ZcvMspaSYIoK7vknBIoRiwONknDV3d3ejtfMwooRichB6Ex0Ers43gFBnbFUWhMHJxzUrAKMpOTM9uEiz1R2tMTx3AHz34x++YynUZQEoQIKAhAAEEIAEQLI6UoChAhHITF1/+erd74aQoDHmcHyMMa7qjcrylJLilOLKp6i1Pp8v3rnovDdmbDsC66ziWVksJOmC+C+ITpZlUkqEkNY6BP8PrvBqVa/XK+9937cpwevr6/V645wbhn6aJoQIIpQx0TTNp4fD8+Gw2+2ur6+ruiCYCaEEz8syMaqMMQAg7+Pl0o7jTCnF/+O//OcY4+WDMc5qY6Z50sYghDjnCEBtTEoJgP9/2jAEwKfIOGeEIgiXIz6CwPvPWYdlsdRaG2OsNhGkGKMxyyQ/v1xMQ4xOGx88ZhQg6Kxzzo3TNI6jtdYHBxFEGHkfvPMQIUYo54xxLoRY2G4IIPvcM8oJJoySpfssJTjPxnofY7LeehcAAAQi751zVjCupAAxaaONcxGAqlxt6h3CKCUAIer7rusaBAClTClprbPWeR+0MQjhpcUNQUgpZ5wpSmqVUwjHqU/RoegBgWW58W6iJCUAj4cTgYkRBAFOCEIEl8+VS5kwmWZjg7HWeedShO04d2PXT33bD/M0WGuMcW3XDn3nzLxZ5WUhV1UmMwZAsMNAMJj6oVASRDAMPUWwUAoiPOmZS045Bx4TAILzs7V5mRcq08ZX9abe3adiC5UAUFAkobHN03vdNxxjhODf/91/0tbc3b8QnCPEkFAJQAhATCDZKeiRCJUgBhAoyXTfTtOEEFQyAwlN06zn2eh5ngdnfaaysq6895fLZRwGTCBIYBwGRqnkQgkBUnLB+xhS9FqbEDxjNKW0TNCBEC24f57nWZYtqg8hWErB/1h/IoRQWZ5leblardZriNBsdNu34zT56AEkICAp1Xq9Xa3WyydIKRWCQQhC8GSp33DOGKsRxoRQ5xZcLiCQjDHROwjTcuVaEjopJY4AiInLHAMIIfRpAZis1gYhVNdVSmmZbkIpId5SSp21fd977zEhSilESVEUxrs5uLbvMibyPHfO9V0XQxCCU7qs9GjZDQnBn5Wc5VQAEgIAYgzAZ/eRckYpTSFqbTAmyxYBEnERMMYQxt3QLxwYQkjPM4AQMhIjwJjGmKILlCJr7TxOeh4ZFUox54zWenmxl+ksgvObq2sQvcxlLti64DjCpunUahe9g15vhLezjQgbra29EOJSIozmkLLZaMYYhjBYZ4zBXDJOtIuEojQBG7xzxjkXA4g++OhAhM4FACImsMhUphSlFHOZYJrGS0n5NDTARyXZMAwYApFJQinB1Dlr3cwYY/maopAJbqJvh0tZv1KimidX1QFVuwgLkECgiq42YnxzfP7opVlvN1999Sc/vvuu79t6tXbxXAmJWBWSxwgFnuGuD6yBcg0BzvM1IPTcXGL0gslVvY/JWjs7P4/ajrMxLhalmnWPMV6tVlzQh6dDN7SIQMbIEsOfvbbOgZQQQksDUoyeUlqWtZRqQdxiTPOstbbLyWIYhufnI8a4KIqyrooy3263CUFr7atXrw7Hx9PpEEJYratNtUaAAAAIoQAAIeRyCP98zwSALD2syz8giAghjGMIaTFNl8Taou0sT79QkhAyDaMdZ4xxst5Yq4ODEFBKU4qLKbjckhFCfDEFMdZae2MhwSkl7awkOKWEARSYUimL5coCgOBsHKfF1pZSlUVtnW7bllCWZRmlJKQIYqIULxMlMMZ61MM8BR8Zg5gQQgLGOM8zjCFCZBrNNE0ApfV2Y43ru26apnmeEEKUM8ylC36aZwBTlmVGW+dc3/fBd/s9+uNeHNq2WYx3gsBms5GcIopzqaQghLCbr4s8z0FKT48fzh/+Dmo3TDPhqizFNIwxQa42LszGeme8UmpBvjxAlNIsK3yYALEhzggDxhimuMyLBJxxHsSQUkrBbqqScyoogwnMky1VHeY+OFuv1l3XDeNIcLIJakBRAkxUBKEUmXXBwAhpKlQZoWr76a7YF7XwweDgICkQ8MaeMcQYRYhwN3RCZX/6539uwzx0jfWOIOzmkbMCQQQAwoTpBOyHx+KFhDIHiAlZ2uCjN2M/YUxv7/ZVVQIQm6aZpuFwOMToN5sN5zLEGGNUgq23Wy4lYdRY65wjnCiGnbEYU4KpMfM4jiklANBCOvR9P03TMjcHY8yYWB7iaZoWhbS9NNa75VXx1khKXt7eaa0554ziGGBKfp4H58LSuY0xjDEihDiXJCTPhYSJLxb0YnJBCKzRCOLFuPHWcSmWF0By4YKf53meJ4YJQxhTIgWBCS4EzsLuIoSUEiGklBKEeEEyZZ4JIRIEKaXoPAyJYMwJ4pyrP2bViuLzKXC5mOZZeTg+hfA5+c85xxhBmJa1PKRIEKCCVqya+rE5N5hi770zdppGzinGNEXS96M2Q11XBHNMCKQk42V0fknZGTPHkAjFS9raWtu2rZ5tlpUYI23nYey0mwTjgiEEUvS23K4gQN6Ezmmbhto7KURdrNVXeVmtxuNDPc2QxubTQ4pQZOm5fcKEYh9Bgs4GABDBGABonINLepHQiCDCeLMufYAEJ0yIdtYH4/WMAS9yUWR5ked26BBGKQAAUV5thtnBCH2khECGKsF3JvhMSoIZ48iFLvauHwYIyHpdSybNYFWZi2rtEQSwR4mnFMbmKBHfXV0/nJ/neZz6PqXgYnj/9t3V3YsVJQBECEhMAAFgOBi+P2flDgsJIJaiVEoRRDlV4zhN01RVlVTV6Th8+PhTSo5gVdebGOPlclJC5CpjhM5j3zG2rC844KxQnEsECWOsqlbbbRiGIQTX970QzlqDMZJSCCGcM13XeR+zLC/LSinlnDs8Pj08flzyRkyK1y/vr3Z7jHHf95eu7YeBMswIjzGChBYmIkbgfUTIE0aV5DylYP3ndyDa6H0IMUUYBKNZkVttIEIQowjSPE5937fNhRNKAOCM5lmBMR6sxghiDGMElDKUkvPGWh0BWubNQIQQJYQQhDDE0LvgnQUAYAABStpMKcQQHBNSUEE4Y4wBkC7NszE6z3MpBMYIIZhSstY45yilOEZvHec8cjJMY9ddAABMcGvtpW3GcTTeMS5CSPM8Vn23qsoEEQAAQSxymVIKITmrIYgYfV7+rXcEEgSmeWxcSOPUJp8iCCQrEeMIpQBS07YMo4hAVtSKljHEvjdN/yErSJ5Jzr+4EdDomfH8cj5058M4DCj1mMBcZdY7hID1BjEAArA2Ao+TTyElRLKYcIgTJBJ4740FGDNVUACyjDOOe90ZO0EXYKYgRAgxWVZVuUkRNVNDVL5/8ZIphbnAhCRrYj+FQsuipEyGqYMSU0WDi4FgAmCIJEHIkUxFnA6XvNjc71jTHht9MXruO/Tx7WOWK8IIAAQAgCCIwTGXit0q4oAhTinEOL26vk4pxgiTDYPthgGorHj98sqb4cOn9xCFSQ9VXa/XOwQCYCzGQAhzzjDGlFIRIIoylNJoJ+ccp7Qo6ywrp2mKMVhrFxB62Tmbxk5zNwxdke92ux2EaOhnFyNhLIBAOBOUPJ+OVMjXr19nZaGd7btxHvwEPUGYc973vVJKygwhHEEkXNAFnMyybMHIjDGEEC65M26RwIQQIQTnPYrAejvPc54VVV4E74PzIQXBxa4qOKMQQuudnmarJ4SQlFKKIpMZAIBL4VNYzlo+hhSSs8EHQ1AMIenZIoooZ8vEFBv854mIZGEHJIRoHMfz+SylXDTd5VpijLEnu2DiKQRKaXCYkEVacGYaIUBCiEiZ1ronZFG3FhpiOUTFmKbJBp8Wq3jZFsahBykaZ2fn1nlJEDqfzxGk63XNMNFmgiKr6lWWV1JKyQXDrOmbvhsH3wzdKQazqorb7f5qvZ39V07Hn96+iVMLgkaUKMHGvnFzDAAizBJMAUKMqNazsUO9KpTgwXtCeUKQc45CoiRZ5yIAKSGZZ0hK7+P66vbq6h5CWhfr4+VZKnXz8kWWZZiwJYTpc82U4pLTBLHRzXghEErKQBIJI4Q0THkIxF3GaN33f/i9zPJVLoD3gJBT30nJlODRBkhiSghB0Lz7wTWPu5d3sCxAAglGweBuvSEED/MEKChtSTGmJBAifvnLXxZl+e3vf3c6nAXPVnXFCA8JRkI+r7nWco5nYxDCm1WJGR7HeRx0SB1jDEIQQRBULF6vtfZ8OZ7P574fUoKci6IolycWQrBer7mg1trT+dwfT9aF1WpVlvliKzk3geBpxkKKi/hmnEYAguiJlNJqk1KijC40n7UWIEgpYYRa7zHC3rqpH2KM5XZLM5IpSSiz1o7WTma2wUTkbwqhJPcRpASe2kfv7csXL0IIdbmqynocRyY4ZiQmAABYeAQpEYQ5iN57S8iUEiSYWacZY9r6y+WSZbIoqhijtT6l6JzXWjvnGGNN0ywMz2DmYRigjwQhyrlxLg7D8iKllKqizLICIswYG+ep7buFDprnGSGklFouG865EDpMIEbcOdeP8/FwOR0bKWWkaQAIQkBVSSlnUknJlSBFtS3KmlKOMeaUWOvret10qW3PXpv2cuxPp7gfttdXdb3zDotKXZ6PTrcIAwwY5Zuha7r27ED0MIWIrI2feVhKjNYIQescRJFTxLhgFJCF3q3W1aqOIGw2q/tXrxFm4zgmCHfb66LIMOEpIiWUTcHFSDjPhEAQ4qLAXObHlsAAkInJYDcnVAAIIbLTNKxv7noQ3/79t/BnX1jr5nmeh/Hm9ZdCZdaMQq0gRHruTscPcTJk0JuNTAkgQG+urh+cMbrDOEjOkMozzrXTzphqlav8KwjJj+9+fH7+ME3nqtzFGDmnlFJGxFKIsthNILqsKBhjZtaTmRP0ICUXLMVovd4uI9KW0J81sapqKeXSHAkhUEoJSTinwzBN87zckv/6r//6+vr69evXnPPj8YwQIC4k6ChB2gxBR0KQt45QigGgVsfF9F0ADG2NtZYRqpR0zmkzazNnUoEQAggY4xQDBKksC6X4NA9N29p5IoQxoaZp6rru+vq2rtbBeyEYl7zt2/bQlqta5Vmm8jovjsfDOLUQ4ggwYSqnBASQUmIc+QhwCJxTAMAwDFrreZ4JYXVdSymapmnby8InhxB8ioRRlXEM0Wi1cy75oENw2kxDDwBo25YJeXV1hUl+Pl3meR7HcVHWlsUgJWCM0XoOITEq+n4chkFbN3R9lpeEE7JmZZkXubre78q6gD7KrFRZxhipqjqlZOapKDLjfF7VggTdcIKgGTuj2+dPjexbiHILrXMRsi1XJEUrEIkozmHsmnnSozEm+GSteTod8yF/cX0DAOz7XmXcWwMYR4jKTDImcrFCjColimojRGa0C963Qx9jHOf+xauXJS+01hBD5F1IEXLJhAhURCTUPk8wWX3EyQEmIeQQYJ8SU9nUdxjABOnvvvvu6ePHeRiiCylFG4PTRugWYOIulxRQYkJbb42hDCXdY5CMtR8ennGCIstxBlwMLvgAQlYWKKHXXyMq6Nu3fxj6SfAZAOS9TSHkSi6gyjiOxoWua27ubr/44gsQy6Y9OudTAvNsJmunaUoJGmPatjXG5VlZlvnp9Pzu3Y8pwaqqyrIEIB6Px6ZpYoxSSj3PHz58OJ/Pwzha65ebSQohBY8ZEEJQwikWkFH8P/9P/0JJiTAOIbjg9aznebbOpJAwRAAAayyBiFKMEUQIcM5TjOPQE0o2m3VVVZwLRlkCaLb21J667swYW6823nlrDCIoK8rtbickD8GP0whiYIwYY0/nB21GxoQQEiMcYpy11sY655bVfVmqF9tgSVJba63VEH4eEkopzYuiLIpMKs44oBhAqERWVlWeZRjBhZXI86IoCs6Y4DymtJjBS4OkMaYfhsvl4n0Mzvdjfzofj6fD8XQ8no/TPDsXIoBSqru7ly9f3nNBEMKUcIjiotBiiFQmyrqSUoYYOaVSUpAiQYEAMzSX/nxhhAc3QRCEyF1AiGJMiU/Req8nN+h+GGZv4zSOn56esiy73l9Z55yznDMEAYSIC1nXmwQRACnLFCVEShlCHLsphoAwUnkWYowgZUUxGa2d40qqsqQiT4ovXGNCGABGsIKkhKiAAMUYCc0ZIfPx8eHjI1bZ8/P56ePHFCNGVKhMZLliHKfYHJ7evXs76CkBp4f+8PAWe2vGQ3s+N22PiSjywgMPYKQIPz8fvfNSqgRjnudKqZSg94ExXpallCJTSmWKEkYo5VJKKRDBLobrq6svX3+ZZXI5/ccYKeUxpq7rFm9USrVa1dPcv3v308dPHwkmL1683Gy2WuvT6WSMZQwTghFCeV7EGJ+enqZpJoSk6K3RzhmQQPAxeJgCEqIgzjnwx2zeIq3M8wxAooSiBLq2hRCuygpE4b2vyzzPS2ttURQAgGmahFB1vb7Z32aZasfxx/c/HZ8eOZeXvn84Pv/iZ692V1cL8nl7e/vh47vHpw/Hw4NghBIeA7FWczlbh7TWwadp0tM0LRsRACGEBMDnTHpKoe/bpdZ0EUyXL+gjISTEwBjbZHIWRlDGKF+e8oV6VSo3Zg4hKSGF+Pzjn2sCYiSIKpULxlNKxlmjp6a7dEM/zWac7DzP06yVzBhXEOJp6FJKmCIw4Xkc+rbbbncq23tr225o23a3qjBn0QccdfN8/Pjhk8w3L76sZzsyiriyFCiIC+8dQY7jFWFDChGkYMwMMLq5uVFKHQ6HfuxjjFywrFoJIcqySgASQhgTjHEMIGMcE+aT8d6JQgrGBRNSqhCjDyElyLOCFxlAPAGQIF/MXJhsAgECDCJOECIEQAKjC+8fHn76ww8fHp8u3Tx2F85QXhSny9l/8+3cd7LIp3kcujaltN/sMefz1H16+z0E+v3hnLH8/v4+QX9+7ku6fXn3BWHifD6eL8eUAuccQfby5T3n7HQ6CSGEYBTjLJNDNzLGXrx8Wdf14XT87f/79z/++FYwqfU0DBoEEEKaJzuM3TA0AICF0eq6LqXkvZWSF0WxFIBut3uE0OVymeauabpF6V+tqqIoBFecyxi9cTaEgCjzCQopZZ7v9htCEJ7HyToDl4GlKTKElgR+CCE6P00TDWC331T7vbX2+fmZc359fe2c+/6HH0JI19fXm3oFUlhtt+v1urmcZ20+HZ4mM7gYCCEUp3ns++Hy4cO7vu8l4yEEDAOldJqmt2/fZlm23W4RIkKIpjlDCDebjbXWe6eUWoKIC2ShlFpSc1mWCSGmaXLGzEuGWkpFi0Jl3vvn5+dxHH1wjLEsyxDCACBCYATJGxsh+IcW1RgBpbSW1TgPjPA8z6WUm81umqa26btuADEwJiCKbXOYppU1M2MiBmRTsGYGYBZClmU5jmPbtgvjDQmlUlab3eHx3Tdv3v5n//nPqVqHPMuJsnEmjCOaBWOj8+OoXQAY05RSSBEAqLgiEGmtp3EGKWGIKeEYcQQZQfT25pYLGl0chuF4OG922wgSxIQTiQmCEBKMCMXbbJN84Ij4SUMBAGEYELBESECMyUMUF9oexAQg4Kp4aIZ//e/+pmlbnhUMezeBAP1kmmksnk8PWKLb9W693hbVqlrVBNF6s5nm+bff/Ns3b978xS//1PrZe1+Vm1W54pLfvri6NIfnp9M0DxCG3e5qu7ldr9cQJghx0zTBGQRwURS/+vVvqqrqxk5y8fXXP+8uzbfffsu5UEoKxRBlmYKMCYzoMHZd12mtlVJ5nmO8WKV4cVchBNbaeTZGB8FzIQSAMaUkRSaZlFLldbWAcbOeUALlqkYJYIhIcH6R7Ze+xeRDcB7E5JyDCWRSSS44JpIrglk39d57IcRCbEspZSY4p4yTSzuGJd2DE+ToZrdbZYXt5uPzp81qmxLsumaejaCCc5FlGYgQOI0okjKDEFsTAbA+TCoTKSVtpqUJI0QnhJCKW+OX1GkIoSiquq6dc8YYxpi31oVknPdNiwjW1jnnGKcC0aIoYozGOMIZRqTtu6kbl3paCOGi+fR9DwCAGGCIAGBlnmWZtNrcXO9SSkZbbQ1IUTHqjI0AAkiBSwlDRPBymV6mV9RFOU0jQghTLHlmkQVQ/fI3/+T+Z7+OkAmmaMYydmWtDcFYCALBgNMQkfMRIWSt1toqmQfvtDaU4DwvCMGz7hBCCIGqLlKCTjtMeYpwnud5nhElOAISY/ARY3w+nAhlcqcAjEPXeGdlWZO8TNyDhCHEESAEBUwIQBAAwgAlCDAhf/lP/ou27f7ut3+TZVm1roALwzA4PwvFZxcqUmx2N9fX1wBGZyPkXlHBCd1uXjCaX93cOYQg56s8G8fmm+/PlAkKeZ6XZVnnee697ftWqXy/v2mai2CMF5mZ7f39q+1m87tvvvn09Hh3fbMqKwqAMQZjGmMchhljXFZyt1u37fbh4TFFLLihlBptlcpfvHixWq0gRCEExqhSGcE8V7xc1SmlZaNYeAIhJKF0Yesg5Xme10XlfRymiYx99zn87PxsTfIBQYgQophwzvf73Wq1WioVDoeDCXa93XJKh2GglL548WK/35dl3ff90rmgtT4cTjHGuq6LQk0YjP1k9cOS/BKYUkGttdMwhpBcsAihPCsvbWPsYTHDEYrW+mnShBDGCCHMOTeN2rmAKeOcF0UlpZ/nmVJKucAQUMqJcykBrXW0vshUtt8FkJJ3C8262M/TNIGY9lebhX0SpIQQfra9tFZKWWu7rivLhUEfhBBCCJAgYwxhaLzRWiNEArIGQQ6pt14wziX3IcCYQEyUMkIhRInTzCGzfvFSrPYA0qwuEwIpAs4UJWKcJzc2zlqcksrK+EQvzdB0Q4xgTN2CQyIEtNZd18WosqzM85wzCUCEmITgAIx21sbMBHEhpVQsJgAQQSUGKXnngrbRWo4oAhRDmJIDELgwwoQQ5AA6CBCCNMEEAYwArve7f/bP/uXr+7vf/t3fAhCr3Xq93gIACEGcy+12J6XgXBmjT8enLCtinijF+6q6v75VSvV9n1JyNmht27Ydhq6u9hDCZYWSkkMIy7KQnHOcHqI/nk+3t7cRoU8PD4SCzbYCJI5DAwBcNvau6zDGGIuunYy5+BhkJu/vX2qtCSGE4DzPr66usiwzxiD0uR1naU8JIbRtWxTFcpBZ2ORpHLTWUkrGxVL2TxmpWEHcOBNCGOc0yyGEOumiKBYKUkq53+/zsnh6eurHISCgqkoI3rddXpb39/fOuaZpEEKHw/PlcinLMsa432+VUn0/Nk0DIRz1RCktimJRlpA1S2BgoXwhwV3fxOAYY0vNGKV0GjUENIRgjNHaLva1kNk09MFogpkN/vl4yFXGCaWcxgj6vkcIKSUXZY0Q0o+TM5pzjBAZu74bxs1ms96sOKHLL5ZzDjCCiJWgKMocQdz3/TAMfd8v14xlFVlkU2/sDIwxJkaDMaY0GmMgSIt5vF5tmsul67rdbgsA8C4iCCjLr25fv3//HnMBCU4YgIAGbRACLtoUnfd+MoZg4QM4nNpp0gDA6EYXA8KYEgwh4Zw9P091tRNCLcTKQoCdz+fT5bK52lKAggvaWJFJQki92mVczG0/2Xm73SKqEmeRURAIgoAC5F0IoUWSIyQAAAvmjACcx/ndH77DFGx3e23mSRsls+vra8EZAIBgTilOPtRFCWGy1sOYxr4HCKEEorcg+mnsx3FMCEIIKeWLUOG0eZ7H1WqVZdk8jmaep2mWVJCEz8+HpmlkLkslIUzWaiFEjGB5Z9brdYxxYUWt5SEEpXJC6FKbrpRcSjSWwTkhLIUociljnee5bVuE4MLvLL7Wkp4BAIzjaPQ8Dv3SsUUwRTHE8/mclSVIkGKihCzzYjkUnf74FVKkGG9XawDiUrrEGBuG4fe///1+v99vtyMhp8PBBnt/f79er62100RTSoiSCMGo5wiByNQ/XGIghJnIEEJ6/lz/ezxetpv9fnfN2bR0whg7AxCllBhjY9zhcPj46UQJL8tSMH65XPKykChqbfu+77puqQZo2nbBa4dh8C4QQpr2YsxMEEQJaAQWMPvcNpSwvMiW2zyCeLk5LWuMUmqJCoUQltD3er3uxyHLCkxJggAlmACY5zn6gBGilP749GNK6epqXxQKAOASZFJxlVWrzbG5HM6nL794RWCcZxtDSAlYa8dJt9Ngg1+mYYcQP7eXweR8gAgdj88Ig7xQi2maUhrnKcsy6x0TvCxrmMDY90rmXddba6s8J7fXerRvf/e7uspYxiAVAAiIKYjB6JERAKkKC+S/GIEgQIidHj5++KEfzznlnNGZzBiTx4dPV1dXr1+/TgksI1xPTcMYBSCMep7nOfnwOH2QUuZ5uQAv8zAv1zNrbZbJssovbdv3fYyxbVs96rqupRB/8Vd/pefp3Ye3s9Fem1nrZT7aer3mXHLOi6JYFiDnuuVvxxgboyll/TD0w3B1vS/zwjlXllUIgTH2x7QARghdXV3FGE6n0/l8Xv5bi6pECLVtm1LKssx60w2ttZYUq/U8Tm4cjbUhBIwgBtAbSynlhFKEm76bpkkpFX3Q02yt9S5Y64/H8zzPgrHmfPbWLh1d7dA6587n89BP3scQAsJxKV/gnC+I9eJpQ0SUEiDC7RYBAB4fH0EilHJrvbUeIOi979oBE1jXayGlEM67mABSShVZSRF+wE9N1/Z9vwA8y60dQkgQHvpRcCalDDQSQrJcLSfm9x/ezcYuUQnjnFSZsW4cR5jA0h+xREBjjMs8ksUWDCFkuQIIWqe558bMjBUYwH6eYwyn0/nm5vbm9vZ0Oj0/P1/tdzG4BIFUtGk6QsjS0Pb7b76rsvzm5noR3JIPIUbtw2Vs+2FgCDNMIgEJAhATQogz5Z21bvrNL35TVaVzBgB1aZuHh4df/OznUsqiKKZp8tqsVqssK/7m3/xWCPHLX/4SJOScCxjGGGFMERIMgAcAgwD9DChPBKNIEkif+x4gdM799j/+DcFhu9nppg3OX++u+ml8fHzI8xxixAhzzkKEmGBWm2maqlVFOXl+PCBKrI8uBADQZne1lNFSDIKHy63y+vp6OV5qbUNIAaRqs37x8ouHjx9na4wxhSx329uq0O/fvz8ez9fX10vbJCEkz9XT0yGEMM9Ga4sQss5TwRHBEKBZm2kcq6qCELZti/FS/88WvtNasxi7S5LRGWuMWZK9M4AppVzIcn9FVquVEkpKNQzDbOYU06W7SCc5lwih59Px6upqv98vyLHWOgR3OjVd1yyHhKwoIITdHwPINYEQwmEYtLaMiZQSAMtPhSXsu1xKGGPO+w8f+iIrlcohhC9evFjGgy6RakKItX4YppTSPP1UllVR5JnI6JoCjKSUy/ekEDElu93OaMsFC853XadUhjEuijwr8uXxXW4gIYTj8Xhu2iUQKYRahKDlTrzoQou1vnzFGFEMBGGlFIJ4GkYz23GYAQBZlrngjDHWmqqq53lepXR9fT21/Tj2AHhMCQB8+Uv79lKvVl9+8UXftma9dtbqcbROazNrrZumORwOECHGuE8REbRghVmWaT3e3XxZF/U4TjHGc3N6enpaZmZRTISUKSWZZYyxcRy9MfsvXkopgbNh7nbXW1muAMwgpjFNEDIAAXDJ44AYRBAjsOSWcAKAUqSk+P7H30UAU0g3NzdMioqSX/36TxhjzaVddvsizymlc9SMSwQgRHi92RZ57r333rVtO03DZrPx/nNHUAgppTgMg1IKQmytzfM8pMil+Pa3f/f3337Dcy5lllf1q7v71bb+sz/787dv32qtlcqqqgYAxJjubuU4jpfmNM+aca4UL4oiK/Jo3DRNy1az3OUWYN4Y471bPsflgLQcxv6hIXOa5k+fHoQQuZR/+OEn0vc9QURxEf5I4YfgEgT7/d4Ys2CohJCx67u+8d5X62q9qU/HS9d1SqkYI8RIWxNCUnlWqDKG4JxTMtdae2+Vkn/UrfDlcmmaZgk3nM/HcUhd33hrpZT77frpcFgqs62eAsaS86oqnAsQovZ8uVwuKsuMMeM41nXNOMWU/OwXP0cILb/nYpRwzgFInLPtdr3UbGRZxhgJzjvnEEKb9fZwOByPx81+t9tsCSHGagjh0I/zPCuliizPpFpe6T/2OwRrLZfKOTfp+enwDDGSEPVjJyRHCM7z9PDwCSFEOT2dG8oZTsD7SCllVByPR4DIi1d3P/xofnr3k2DicjxpZ7Vx7356+4fv33RtuzRdLrgrhBAAaO1MCImANN20v1IJAq0nLtjrL75cWo6X9iEIYQRgGro8z3fbfXQeMMKqPJ4tZCQSnKJGCABgIGSR8Zi8AjDCACOFKKUEAIQghT/71W/+09/8Xx+ePv7mN39GKYEQUkoIhFJKyViMUQrBOY8xWGMwxhAmIeRqlWWqSCl9/PQeY6JUHkKilNZ1babZe7/dbsdxfP/+I4HIp7jfX724uX06Ht6/ffezr7+sd2tJ2KC1dvrNmzcI4fv7e4yotXYaLQAAJCIl1VrHANbr9dXVlVLq0jQkQci5s3YZB7asF9M0ns/nZSdfunCWo8ESUUQEG2cfH54wxirLOOcuxogQefPTj9fbHYgAYywEJ4Qkj/tp5Jzf39+v15sff/zx48dP3hghWVFk5/N51hqgRCldrhRCSSp4mq33Xgqm5xkAwATxASks1uv1Z2bB+5TSYlwTQmKsi6LQ2i49EZ8eH3/66ScAQL1eEYQ3VRlTgBgRgiggS0wzAXAZuufn53meN9v1er1mGU/WxxgXWL8oCgwRALEs63k2TXO+e3ld5JUz3uGQ0rz0iYMUpeCz0dG7alVfLi6lVGU5xyTL1KJd1HnOOY8x9nrCES1dQ0Iw42xzuQxDRxH0wW2329Vmq4auG3rJWTd202RUUSxmZAgDQihCeDxcNnsQIHjzhx8VlXrUx/bSNM133/zudDlCBGKKkCHCMCYJQWy9W7ajeZ7rekMpNWa2zuZZIbl4OD4P88gVRzNYAFkAowfAaG28486WZT233TQNsibIeoARBCwBw8scWh+mMxY5QGR5AELUODqYwN3di2N7qsui67q2bQUjlNJ5mpaTISIQY5BSyhXnUmo3Ns9HQfJqvaaU5nnOqAjROeeEEDHGsqy+LAouBMa4bZqU0jLT9/B0/Pa77+5e3BRlOTa9qOvonfX+eDxqred5WubeLfdgY/U0TVmWXV9fO28AAAhBwbme56XvZIkrLUuV1maepwX0XyqjFw0wxlgUxVIQeHtzAyHMsqyqquV8S1ab9cfHB4rwyxf3/6BnXV1dhRDevf/w/PzcdR2hFGIICXDAG+0ul0uWFUJw7/04ThFARpmDxls39NOnh49SykU8dcE2TbNkzxcbixCyKIxLs2mK0BjnfYAIbfdXECCWZQTEfpxCSlRKTEj0EQOUgIeYvn7xcr/aPD09nY8nEJOeRiklYRRjiDFVSmRSLbDnPPsvXr+kFOt5pJTvylU30BCyGMHt7W2M8XS+HA6HT58+rddrxljbdEVV5HnunBGKL4pE0zTSk6qqAEDG2ZSST5Fh/O7du+Pl4q3ru2EYp8fnpyrL9tuNc+79+3cY4zwrF40igs+diuM4Dto5Q85N0/ft7//w3dPxKRizOBUxBUQJIjB9rsdjQijGiFIC4vR8+pTQLqVEMD+dTtY7gNLxfJKYXF1dCcGbRi/t1v3pElKo1quCy/PTaeMhxgBAhuBMhPQa6+aSMeS0JmsBEYvRwziCaNvu+POf/7x38+nS3FzdLLkOzrmQyloPCcyFjBFIqdTSMzeHcTBd9Je+l1Ku12tKsB6tlJIxrrVmnJdVxZRc1O3L+dz3/ay1c+EXv/rlalUNY08JtyFgiEKC/+gf/UWM8XA4LDVeT09P3juE4DgOXd+/evUqz68uzfndu3dKZVmWLQtElmXzPJ9Op3mepZQxpiWGtcyPybJseRiEEPM8E0KKokwpLYhNAiAvMvi//2//yzxNL+9eIIoxxlwKiolk/NCcg/PjOHIpmJBN14AUotfBxbquBVddN3yG8jEM0XEsIQqjHpc2oaXlYhgGZ4O1njFGKVaZ8C7GCDCGZp4TxNpYrXVWFlmWVauaMXZ8PpxOJwDAl19+KYRo29YZbbWFyVNKIaY+hhSBdWYYOinl8i7FGANIeZ5LxjkjDCOAkY+ha4eube9v7+/v7y9tb6xLMSzER0rJOv/+/fuYwmpVLSzgarWSkiO0xLRdjHGeHYYAQ0iYcM7FZL3XAJBPh/PHDx+maRjnqV6tqmKVEalH3c391X67qncwwtmaCELEyQVbyMKH9OndWzsNAIPH54OZDeWUcCYwRQgFknimYEwQAEIIZpLACIONPiDMGZUAACnl9fULyNDlcoIJ7Nab+xd3hJCPD59Op9PPvv757c0Npagu8xhBP2rGGGWCSsIghggDgH74/s2kRyH53Rdf7+9+naCz3UfobNv2wEXC6OnSKyVi8tY57xzBIgFUrdfb/f75wwfFGaD4dDowBIui4GUOQjyfz/M0FUWx0LVOuzzPAQgfHx8YY9644+XsQ+BKcs6//PpnmNI3335DES7yXEopVe59RIxGn7rzgTJMMBvnqWvPhJCqXrVt672/f3GHMXbOU8KPp6dxHJfelGmaL5cLIWSep6VvHUKYZWrZFqx1V1dX19fXTdsOfRdjzPPCOTeOwziOjEqSUnr16hVFuO97ANI0Tc45CJOUkhHy2RgmIczWewdAHKd+MRdm3Vvj9/t9vSqXNi9vPaWcUL7sg957CLC1s/fxq6++AiB2XbeItcbMEGMplFDy+clmWbbZbEII5+PpeDwihF69eiWlfD48heAo5YxzRiXnPALorC/qylrLLvJ8OdlgFVd5nldVxTkPwXHOY3Baa+8jk4LY6dge2r4fxzHLJcFsmqYsl1zIkOLN7XXTnJ+fH7fbLefSOcM4WsrkVCY4k08Pzw8fP/7851/LXDZNQ4jK8y2l/Or6+tdffx1jbLrL4+F5mqZjc2zaLvo0menTw5MS2Tj2MhOY0afDUwrp6ua26RsMoJBytds64yilxs+rqv7i/iWgWM82WmetdSmB5L33EZIIowvhfHi2eqYAgQgRBU8Pj0W5Qphr99YMU/Kpt/r9pwelFBfM+zgM4/l82uzWIH5mn4wxl0tjXeq67tKdXn368F/90yqA9PGn9xlndV244BlWLzO1fPNktHNUz1YIlYI9Pj8RAphkmIg8c9GNAKTT84Fzbo0JwS/uVZUXKcQfnx4Z4cfm6FPMs7Is1hhDxokQ3E0TzjIC0el4BBESKqGNQmUgJQjHelU455umoZwCAId+ZphyTH78/s1/+Pd/e3u3L4taiCzEeD4dCSGMsWmazuczhDDP87quq6pyzlHKlFLe+3fv3v34449v3nxfltXd3Z2Qsm3bcRg4Z2VZOh/g//l//K9lXozdQAgBGFlrFmJ78RQWjmDRiABIXDI9j+M4LmrmMt2yLEspZdc1zoWYIOccgHg5H6dJV+VGSAIhrOsyJZgiXIyJz1WVGLf90Lbt1dVVVhbehSUMSghJCQIAilJxyWACACCK4DiOq9WGCv7xw6du6AXjEIFx7oLzGMAUP3fIYEKcc0rKaTYepqU57Px4nuf5xd3d/f3tdrdmjMQEz+fzsiE+Pj6+f/9+s9lBmDjndb0WgmEExnE+PB5+ePPjz3/x5d3LmxhjSpBS2nVdSuHu+s650HWNsXNKyfoICBdC9M3ldLowwoVgxhhE6DzP796+DzDe3d3lWTFMA0AQJpASmPXw9PAopfzqq69wRE4b7b0DUc+dc8maABKaTD+Og9F66oeyWM3z2DaXBDBgZJ6GdbF6dfsyr2pjxryQq9VK8Ayi9OHTByEYISyTmcoViGnoZybor375i5gQimF/d72qSmOD1Y4R0rsxGM9JoJhJKcdZ+wiW3vMQvA+RMxpCXITFEPyi73kfy7LEhH7zzTfN5fL16y93m+3jh4+U0ojgsT3X1ebV/T0AoB2a9+/fG+v+6q/+CmP85vvvEYKvX7+OMZ4uZzdpwslqtcmy/Ntvv3nz5gfOOYRgXdVaG4yxKvLnp3PXNVnO1tsNxZxzttwBjsdjlmV3d3fL5K+6rpumAQAyKhZ15P379yoT+911UZUAxGXIQFEUWmtCEH56esqL0qVIMCmq8vD0HJxBhPX9WBRZlmXD0CEEiioHMDJSL8RY1/dCynEYPn78eH11y5iA0C2yt3Mmxfrx8bsiX+1317MevY+cSRdD03SEoKKohGAAAB/TItPqcTqczkVRLGaW99E5V5VFgqntLpRSToRSyjnjnOGCbHktJVdKHc/8+fGpa1pn7YsXL7gQ0zQtTcU+hHnoCaNXuy2IKdicMSqlLMrydDr1fe+cK8qMEHJ//8oHq2e/Wq0YY/M8T9NACYoRjOMo86xebwlhC5K9jOv68OHd8fn55f0XxhtKCcVkIyRCBFNUSVxXWVFUnNPLpU0JxgSvr/dN01jtSqU2qzqAABEJIYxtF41zztlZf/HyVd+2BYJEiVmPdg7OBOcMZdcQoWW6aNd1/TCpqths1j6G4+ECExqCjcMpRO+TpVK24yiEYEpZH4UQTGVcyirLvnxdbfcbiEBZrLJ10R/OTnsmCQHAmZBLhTkMUc/j1HXDarfnBNfVimAUrEsoDcPUdR0hSAgxaRtjFFwCYKZpDnHcbbdSiLZt53HCCDGG91f7/YvbH968+df/+v9e7zYQI+t8VRZtc6mqOqXofbpcTgvd2DT9p0+fbm5v/+TXv1JKSCkZFYRhlxJTGed8s9mUxZpxRAju+yEBEP7IE9ze3iKEP3z4yDlfreq2bR8fHy+Xy93di+1mnxLmPJumISV8Opwpg+v1Ossya+2njx+Icw4AICQ/XRoc7DyCy+VEKV1VFSMoek85bs7HABIXeL2uQWIfPnwYh3m730AIOedPD89a6215RchcZiqBQCAvbgqEiLF+miZCcYwRAcAJBUpYa9u+67qIMQ4hwQQYY1LKSZuiKJbqeqm4gnwapyxXVVFP02CtlVIiBIZhgDBVVUkImfXIOf/FL35xuVzmec6lWlAqToWxfoneG2fKstxtiqVrqR87emLLYDaEQQguRMIovb66/eGHHwGIAMTj8ZlSKiQ7PTfDMKy3u2V65KKpYQwxxlW1cmbGBF5vb/pp9N5DSkKM3amBGArGjJn7oQkh5Flxupy9i1e7/en59P79269/9mVZVKfTqWnOdjI3V9dZliUQMIEyl8M46nHYVGu0IiklH8PSAx5cTBEwTqXKpMwwBC54bx1KaJgHn2xRFDEAY6y1tu9aCFBdr3PFpVBSCCmFzAQC2e76+nj+8PHHN+u8hADhBFCR4RxmHGMqzDhlymKIHIgQIWu9CRGBGH0AIZZZPo7Tsuo3l/b56SQkgxC2TX97e3t7dd00DSHkdGnbaboR3Bmb55m1M2H0xd0rPbmhO/zwwx9mbV+/fg1A/Hd/+x//8Ic//PxXv/7Hf/GPf/2nf6Gnwdn48v4rITJE0KTdu/cP96/uL5fTv/qbfyUYrYry9ub+5cvXTOBxGqZhbJpms9n87ne/+w//4d//5V/+pdam61pK6Wq1enp6fHH3cr/fv3373nmPEZKyPDx9TN7D7fbh4UEojv/7//a/hhCNw4ghnMfJGrPbbTHGzmoIgfduGPqirqqqYoxCiLTW5/NpHKb1ZmOtQwhKpWJKjAoEodFTiN5a1w8TowJhfL4cg/dOm3EcUgrGmWEaOOeccUIIYzzBpeCXZnmOKVlkSoSi9zaBBEBijAMAI/Da6piSUBJjSggdhrHvej3PzrklAmysnbTx1hFMpllPg0YQgxC44NaEGEBMyXnLmVyW56JQy6RbAMAw6NPprJQqy2Ih56TIz+eWUiwLFZKry4JzlmDyITAqyrK4ub6ilEzTDDChlGnnvHcxQcoYofTSdjH4oijmafYuCMGnfmKSC8WneSjzYhwHM42qyGMMXd8yxgCEmBBnreQCgtS3zTyN3juMSNe2IIS6KF6+eCE4JwAiiBBBTg8E+lJxSmUKSU8TiklxcbXdffX69e319Xazvbu7IwAaZwkmbrYI43c/ff/m+zdCZYSLrNhk61vGJdDzbMboIWecSwEg5Jx774zRCQBKsMpy56K1xlvnfdis11VVKqWUUtvNLlfCGHM4HKjg6+02ePv+3fu2GQhRVBTb3bV3bp4Glme7/S3A9MuvvwogGWteffHF9fWNyPg4ztpo7xyAUOXy0l0uzYUQdn19k0mhMrmpd+M4z3MPEnj/+L5vOyUlY6zrurKu9rv9999///79+7IsheA3NzcY4/cf3k1TlxeyXlXeG22m11+8aprmm2++4VxQLvD/8N/9U0Lw8/MhOi84325W9WplrQ0AuBD7cSKM13W1Wq2MsdOsAYhZlmdZyTg/HI4LnLTUXTFOQ0rGBYCwtdb7oGTmQ5pHDRNGGG23G6EkpKxarbMiI4zmWT7P82czz3k96wUG8N6H8LmP2nvHGCcMW2MXO9l6H2KkhBBCGaPDMMzzvOBJZVkF5zEhlBIIYAiOYEAYLfJaycxoDSB4/cVXq1UNoGOMLROQCIV9PzrnEYIh+LKsEYIhRAjherOq6nWeZxBFZ10CEBICMIQpphj7vh+nKZMZhoginKlc5RnCKCWsZMYZRxBCiLOswBhZbazzV3c3Yz9aYzjnMCXj3FKKTymlhCwVfZzzZXCBEGK73rx8cbddrynBeakYJ0M/6lnPeux0Z52Z5mkcOklzQulmvd7v93Vdf/HF6/V6XRS5sc57v91t9/s9gcS5eehP82gSRC+uXm1vXpKsiNp4azxEwflh7DDBjLNxHEIIUgrnnMozRJE2gctsvakSjPOsjdFLl3DX9W3bTPP0zTd/75wpyurjp4cY/fX17uXLO4jSp09vN7t6tvqbb3+/vdozwa6ur4yZxrG/2u/zvLTGPzx+HNpxmsdxGvq5f/v+/eF4cs6+uL+/uXrVXC7bXbVd7zbbOkQbE9ls1ymkw+FAKQ0hIISlkAjh169fU0rbtoUQ3d7eCCE+fvxwOh+8C1mm6nU9ztP/82/+bT8M9WpT5DX+F//Nf2mMCSnIXAnBQ4zW2o8fP3oPKBdUCABhe744rRGGEcRpnNarHaE8xlRV9TKPmxA6ji3AmHGVEGZcMsoylcMIZJWDhJWQr169yDIBEMKUqSyfzey8J5SY2VwuF8bYknjCGFFC26azJkghAUJKSUpJDDHPs0U1hwBsN+u6rqQUy3yohfWjlBKC6/WaYAwhpgRXVZUVuXM+L9V6Xdf1CiHSdX0IwdjJWgsS9j5A7AEMw9AppTKljNHB2364VHXxZ3/+Gy4yKSWBAELMVQYwjNEB6yCEEGOVZ1JJRgmljHGZYqKEI4ApoUvBBecsxnhpm/PhADGqq5W15vnpKQHQtsM0jff394v4vWxiCGNtjJL59c11VVXzPFtvdtsN5+zx6fF4fE4xjV0fk6WUb1bXRbZ9ef/Vr//0T/b7fVXV9bquyoozyZlIKaYYMi4QIefz+dJeTHCHw1Hm5S+++pnDQAiaYtCmzQRGmFPOheQQY+98SkAICSG0ziWERF4hTGMK3dAzySglGGEfUtNcLpfLbGaMgBRsu90gTJf5yhhTZ+PhcHr/7h0ipKxXCZMiUx8/fqSEMUa7rg3eh5Tevf+gRPaP/uLPbm9eluWGMFHXq6qot+v9/auvEMaEYEyID05yFUMqq6yudxiRFKG1/ssvv65XmyIr7+7uEMTb7eb169dKyaIob+9f5ln+5s2PZnS//NWvrm5vfvf77/72P/77oiwRBFyo/w/zeqVasnQdGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "256×256 Array{RGB{N0f8},2}:\n",
       " RGB{N0f8}(0.153,0.271,0.137)  …  RGB{N0f8}(0.125,0.231,0.102)\n",
       " RGB{N0f8}(0.153,0.275,0.141)     RGB{N0f8}(0.137,0.255,0.122)\n",
       " RGB{N0f8}(0.145,0.278,0.133)     RGB{N0f8}(0.149,0.275,0.129)\n",
       " RGB{N0f8}(0.157,0.278,0.118)     RGB{N0f8}(0.2,0.345,0.184)  \n",
       " RGB{N0f8}(0.165,0.275,0.118)     RGB{N0f8}(0.22,0.38,0.216)  \n",
       " RGB{N0f8}(0.161,0.271,0.114)  …  RGB{N0f8}(0.251,0.4,0.239)  \n",
       " RGB{N0f8}(0.165,0.267,0.114)     RGB{N0f8}(0.275,0.42,0.259) \n",
       " RGB{N0f8}(0.176,0.278,0.125)     RGB{N0f8}(0.251,0.42,0.251) \n",
       " RGB{N0f8}(0.184,0.294,0.137)     RGB{N0f8}(0.278,0.443,0.255)\n",
       " RGB{N0f8}(0.173,0.282,0.122)     RGB{N0f8}(0.294,0.447,0.263)\n",
       " RGB{N0f8}(0.173,0.286,0.106)  …  RGB{N0f8}(0.286,0.459,0.267)\n",
       " RGB{N0f8}(0.176,0.282,0.114)     RGB{N0f8}(0.286,0.459,0.267)\n",
       " RGB{N0f8}(0.18,0.282,0.129)      RGB{N0f8}(0.267,0.439,0.231)\n",
       " ⋮                             ⋱  ⋮                           \n",
       " RGB{N0f8}(0.804,0.761,0.69)      RGB{N0f8}(0.706,0.643,0.58) \n",
       " RGB{N0f8}(0.753,0.71,0.639)   …  RGB{N0f8}(0.698,0.651,0.588)\n",
       " RGB{N0f8}(0.612,0.584,0.51)      RGB{N0f8}(0.89,0.839,0.776) \n",
       " RGB{N0f8}(0.678,0.659,0.58)      RGB{N0f8}(0.91,0.867,0.796) \n",
       " RGB{N0f8}(0.643,0.596,0.533)     RGB{N0f8}(0.831,0.776,0.725)\n",
       " RGB{N0f8}(0.639,0.592,0.529)     RGB{N0f8}(0.843,0.796,0.733)\n",
       " RGB{N0f8}(0.706,0.663,0.592)  …  RGB{N0f8}(0.851,0.804,0.741)\n",
       " RGB{N0f8}(0.722,0.671,0.604)     RGB{N0f8}(0.749,0.698,0.631)\n",
       " RGB{N0f8}(0.702,0.651,0.584)     RGB{N0f8}(0.78,0.729,0.655) \n",
       " RGB{N0f8}(0.694,0.643,0.569)     RGB{N0f8}(0.773,0.722,0.659)\n",
       " RGB{N0f8}(0.694,0.643,0.569)     RGB{N0f8}(0.788,0.733,0.69) \n",
       " RGB{N0f8}(0.671,0.62,0.545)   …  RGB{N0f8}(0.741,0.694,0.647)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load image for prediction\n",
    "img = FileIO.load(\"images/cat256.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabby, tabby cat\n"
     ]
    }
   ],
   "source": [
    "# get prediction\n",
    "prob, class = classify(classifier, img)\n",
    "println(class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the predicted class is tabby, tabby cat. We can also plot the top 5 highest predicted probability. If you didn't install Gadfly.jl, this step could be omitted. Currently (before Julia's static compilation is implemented) it takes a very long time to load Gadfly.jl."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\r\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\r\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\r\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\r\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\r\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\r\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\r\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\r\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\r\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\r\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\r\n",
       "(function (glob, factory) {\r\n",
       "    // AMD support\r\n",
       "    if (typeof define === \"function\" && define.amd) {\r\n",
       "        // Define as an anonymous module\r\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\r\n",
       "            return factory(Snap);\r\n",
       "        });\r\n",
       "    } else {\r\n",
       "        // Browser globals (glob is window)\r\n",
       "        // Snap adds itself to window\r\n",
       "        glob.Gadfly = factory(glob.Snap);\r\n",
       "    }\r\n",
       "}(this, function (Snap) {\r\n",
       "\r\n",
       "var Gadfly = {};\r\n",
       "\r\n",
       "// Get an x/y coordinate value in pixels\r\n",
       "var xPX = function(fig, x) {\r\n",
       "    var client_box = fig.node.getBoundingClientRect();\r\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\r\n",
       "};\r\n",
       "\r\n",
       "var yPX = function(fig, y) {\r\n",
       "    var client_box = fig.node.getBoundingClientRect();\r\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\r\n",
       "    // Traverse upwards from a snap element to find and return the first\r\n",
       "    // note with the \"plotroot\" class.\r\n",
       "    Element.prototype.plotroot = function () {\r\n",
       "        var element = this;\r\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\r\n",
       "            element = element.parent();\r\n",
       "        }\r\n",
       "        return element;\r\n",
       "    };\r\n",
       "\r\n",
       "    Element.prototype.svgroot = function () {\r\n",
       "        var element = this;\r\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\r\n",
       "            element = element.parent();\r\n",
       "        }\r\n",
       "        return element;\r\n",
       "    };\r\n",
       "\r\n",
       "    Element.prototype.plotbounds = function () {\r\n",
       "        var root = this.plotroot()\r\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\r\n",
       "        return {\r\n",
       "            x0: bbox.x,\r\n",
       "            x1: bbox.x + bbox.width,\r\n",
       "            y0: bbox.y,\r\n",
       "            y1: bbox.y + bbox.height\r\n",
       "        };\r\n",
       "    };\r\n",
       "\r\n",
       "    Element.prototype.plotcenter = function () {\r\n",
       "        var root = this.plotroot()\r\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\r\n",
       "        return {\r\n",
       "            x: bbox.x + bbox.width / 2,\r\n",
       "            y: bbox.y + bbox.height / 2\r\n",
       "        };\r\n",
       "    };\r\n",
       "\r\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\r\n",
       "    // does everything right.\r\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\r\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\r\n",
       "\r\n",
       "    for (i in events) {\r\n",
       "        (function (event_name) {\r\n",
       "            var event_name = events[i];\r\n",
       "            Element.prototype[event_name] = function (fn, scope) {\r\n",
       "                if (Snap.is(fn, \"function\")) {\r\n",
       "                    var fn2 = function (event) {\r\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\r\n",
       "                            return;\r\n",
       "                        }\r\n",
       "\r\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\r\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\r\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\r\n",
       "\r\n",
       "                        if (reltg != this.node) {\r\n",
       "                            return fn.apply(this, event);\r\n",
       "                        }\r\n",
       "                    };\r\n",
       "\r\n",
       "                    if (event_name == \"mouseenter\") {\r\n",
       "                        this.mouseover(fn2, scope);\r\n",
       "                    } else {\r\n",
       "                        this.mouseout(fn2, scope);\r\n",
       "                    }\r\n",
       "                }\r\n",
       "                return this;\r\n",
       "            };\r\n",
       "        })(events[i]);\r\n",
       "    }\r\n",
       "\r\n",
       "\r\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\r\n",
       "        if (Snap.is(fn, \"function\")) {\r\n",
       "            var el = this;\r\n",
       "            var fn2 = function (event) {\r\n",
       "                fn.apply(el, [event]);\r\n",
       "            };\r\n",
       "        }\r\n",
       "\r\n",
       "        this.node.addEventListener(\r\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\r\n",
       "            fn2);\r\n",
       "\r\n",
       "        return this;\r\n",
       "    };\r\n",
       "\r\n",
       "\r\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\r\n",
       "    // This is a function to directly update element attributes without going\r\n",
       "    // through eve.\r\n",
       "    Element.prototype.attribute = function(key, val) {\r\n",
       "        if (val === undefined) {\r\n",
       "            return this.node.getAttribute(key);\r\n",
       "        } else {\r\n",
       "            this.node.setAttribute(key, val);\r\n",
       "            return this;\r\n",
       "        }\r\n",
       "    };\r\n",
       "\r\n",
       "    Element.prototype.init_gadfly = function() {\r\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\r\n",
       "            .mousemove(Gadfly.plot_mousemove)\r\n",
       "            .mouseleave(Gadfly.plot_mouseout)\r\n",
       "            .dblclick(Gadfly.plot_dblclick)\r\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\r\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\r\n",
       "                  Gadfly.guide_background_drag_onstart,\r\n",
       "                  Gadfly.guide_background_drag_onend);\r\n",
       "        this.mouseenter(function (event) {\r\n",
       "            init_pan_zoom(this.plotroot());\r\n",
       "        });\r\n",
       "        return this;\r\n",
       "    };\r\n",
       "});\r\n",
       "\r\n",
       "\r\n",
       "Gadfly.plot_mousemove = function(event, x_px, y_px) {\r\n",
       "    var root = this.plotroot();\r\n",
       "    if (root.data(\"crosshair\")) {\r\n",
       "        px_per_mm = root.data(\"px_per_mm\");\r\n",
       "        bB = root.select('boundingbox').node.getAttribute('value').split(' ');\r\n",
       "        uB = root.select('unitbox').node.getAttribute('value').split(' ');\r\n",
       "        xscale = root.data(\"xscale\");\r\n",
       "        yscale = root.data(\"yscale\");\r\n",
       "        xtranslate = root.data(\"tx\");\r\n",
       "        ytranslate = root.data(\"ty\");\r\n",
       "\r\n",
       "        xoff_mm = bB[0].substr(0,bB[0].length-2)/1;\r\n",
       "        yoff_mm = bB[1].substr(0,bB[1].length-2)/1;\r\n",
       "        xoff_unit = uB[0]/1;\r\n",
       "        yoff_unit = uB[1]/1;\r\n",
       "        mm_per_xunit = bB[2].substr(0,bB[2].length-2) / uB[2];\r\n",
       "        mm_per_yunit = bB[3].substr(0,bB[3].length-2) / uB[3];\r\n",
       "         \r\n",
       "        x_unit = ((x_px / px_per_mm - xtranslate) / xscale - xoff_mm) / mm_per_xunit + xoff_unit;\r\n",
       "        y_unit = ((y_px / px_per_mm - ytranslate) / yscale - yoff_mm) / mm_per_yunit + yoff_unit;\r\n",
       "\r\n",
       "        root.select('.crosshair').select('.primitive').select('text')\r\n",
       "                .node.innerHTML = x_unit.toPrecision(3)+\",\"+y_unit.toPrecision(3);\r\n",
       "    };\r\n",
       "};\r\n",
       "\r\n",
       "Gadfly.helpscreen_visible = function(event) {\r\n",
       "    helpscreen_visible(this.plotroot());\r\n",
       "};\r\n",
       "var helpscreen_visible = function(root) {\r\n",
       "    root.select(\".helpscreen\").animate({opacity: 1.0}, 250);\r\n",
       "};\r\n",
       "\r\n",
       "Gadfly.helpscreen_hidden = function(event) {\r\n",
       "    helpscreen_hidden(this.plotroot());\r\n",
       "};\r\n",
       "var helpscreen_hidden = function(root) {\r\n",
       "    root.select(\".helpscreen\").animate({opacity: 0.0}, 250);\r\n",
       "};\r\n",
       "\r\n",
       "// When the plot is moused over, emphasize the grid lines.\r\n",
       "Gadfly.plot_mouseover = function(event) {\r\n",
       "    var root = this.plotroot();\r\n",
       "\r\n",
       "    var keyboard_help = function(event) {\r\n",
       "        if (event.which == 191) { // ?\r\n",
       "            helpscreen_visible(root);\r\n",
       "        }\r\n",
       "    };\r\n",
       "    root.data(\"keyboard_help\", keyboard_help);\r\n",
       "    window.addEventListener(\"keydown\", keyboard_help);\r\n",
       "\r\n",
       "    var keyboard_pan_zoom = function(event) {\r\n",
       "        var bounds = root.plotbounds(),\r\n",
       "            width = bounds.x1 - bounds.x0;\r\n",
       "            height = bounds.y1 - bounds.y0;\r\n",
       "        if (event.which == 187 || event.which == 73) { // plus or i\r\n",
       "            increase_zoom_by_position(root, 0.1, true);\r\n",
       "        } else if (event.which == 189 || event.which == 79) { // minus or o\r\n",
       "            increase_zoom_by_position(root, -0.1, true);\r\n",
       "        } else if (event.which == 39 || event.which == 76) { // right-arrow or l\r\n",
       "            set_plot_pan_zoom(root, root.data(\"tx\")-width/10, root.data(\"ty\"),\r\n",
       "                    root.data(\"xscale\"), root.data(\"yscale\"));\r\n",
       "        } else if (event.which == 40 || event.which == 74) { // down-arrow or j\r\n",
       "            set_plot_pan_zoom(root, root.data(\"tx\"), root.data(\"ty\")-height/10,\r\n",
       "                    root.data(\"xscale\"), root.data(\"yscale\"));\r\n",
       "        } else if (event.which == 37 || event.which == 72) { // left-arrow or h\r\n",
       "            set_plot_pan_zoom(root, root.data(\"tx\")+width/10, root.data(\"ty\"),\r\n",
       "                    root.data(\"xscale\"), root.data(\"yscale\"));\r\n",
       "        } else if (event.which == 38 || event.which == 75) { // up-arrow or k\r\n",
       "            set_plot_pan_zoom(root, root.data(\"tx\"), root.data(\"ty\")+height/10,\r\n",
       "                    root.data(\"xscale\"), root.data(\"yscale\"));\r\n",
       "        } else if (event.which == 82) { // r\r\n",
       "            set_plot_pan_zoom(root, 0.0, 0.0, 1.0, 1.0);\r\n",
       "        } else if (event.which == 191) { // ?\r\n",
       "            helpscreen_hidden(root);\r\n",
       "        } else if (event.which == 67) { // c\r\n",
       "            root.data(\"crosshair\",!root.data(\"crosshair\"));\r\n",
       "            root.select(\".crosshair\")\r\n",
       "                .animate({opacity: root.data(\"crosshair\") ? 1.0 : 0.0}, 250);\r\n",
       "        }\r\n",
       "    };\r\n",
       "    root.data(\"keyboard_pan_zoom\", keyboard_pan_zoom);\r\n",
       "    window.addEventListener(\"keyup\", keyboard_pan_zoom);\r\n",
       "\r\n",
       "    var xgridlines = root.select(\".xgridlines\"),\r\n",
       "        ygridlines = root.select(\".ygridlines\");\r\n",
       "\r\n",
       "    if (xgridlines) {\r\n",
       "        xgridlines.data(\"unfocused_strokedash\",\r\n",
       "                        xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\r\n",
       "        var destcolor = root.data(\"focused_xgrid_color\");\r\n",
       "        xgridlines.attribute(\"stroke-dasharray\", \"none\")\r\n",
       "                  .selectAll(\"path\")\r\n",
       "                  .animate({stroke: destcolor}, 250);\r\n",
       "    }\r\n",
       "\r\n",
       "    if (ygridlines) {\r\n",
       "        ygridlines.data(\"unfocused_strokedash\",\r\n",
       "                        ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\r\n",
       "        var destcolor = root.data(\"focused_ygrid_color\");\r\n",
       "        ygridlines.attribute(\"stroke-dasharray\", \"none\")\r\n",
       "                  .selectAll(\"path\")\r\n",
       "                  .animate({stroke: destcolor}, 250);\r\n",
       "    }\r\n",
       "\r\n",
       "    root.select(\".crosshair\")\r\n",
       "        .animate({opacity: root.data(\"crosshair\") ? 1.0 : 0.0}, 250);\r\n",
       "    root.select(\".questionmark\").animate({opacity: 1.0}, 250);\r\n",
       "};\r\n",
       "\r\n",
       "// Reset pan and zoom on double click\r\n",
       "Gadfly.plot_dblclick = function(event) {\r\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0, 1.0);\r\n",
       "};\r\n",
       "\r\n",
       "// Unemphasize grid lines on mouse out.\r\n",
       "Gadfly.plot_mouseout = function(event) {\r\n",
       "    var root = this.plotroot();\r\n",
       "\r\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_pan_zoom\"));\r\n",
       "    root.data(\"keyboard_pan_zoom\", undefined);\r\n",
       "    window.removeEventListener(\"keydown\", root.data(\"keyboard_help\"));\r\n",
       "    root.data(\"keyboard_help\", undefined);\r\n",
       "\r\n",
       "    var xgridlines = root.select(\".xgridlines\"),\r\n",
       "        ygridlines = root.select(\".ygridlines\");\r\n",
       "\r\n",
       "    if (xgridlines) {\r\n",
       "        var destcolor = root.data(\"unfocused_xgrid_color\");\r\n",
       "        xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\r\n",
       "                  .selectAll(\"path\")\r\n",
       "                  .animate({stroke: destcolor}, 250);\r\n",
       "    }\r\n",
       "\r\n",
       "    if (ygridlines) {\r\n",
       "        var destcolor = root.data(\"unfocused_ygrid_color\");\r\n",
       "        ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\r\n",
       "                  .selectAll(\"path\")\r\n",
       "                  .animate({stroke: destcolor}, 250);\r\n",
       "    }\r\n",
       "\r\n",
       "    root.select(\".crosshair\").animate({opacity: 0.0}, 250);\r\n",
       "    root.select(\".questionmark\").animate({opacity: 0.0}, 250);\r\n",
       "    helpscreen_hidden(root);\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "var set_geometry_transform = function(root, tx, ty, xscale, yscale) {\r\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\r\n",
       "        yscalable = root.hasClass(\"yscalable\");\r\n",
       "\r\n",
       "    var old_xscale = root.data(\"xscale\"),\r\n",
       "        old_yscale = root.data(\"yscale\");\r\n",
       "\r\n",
       "    var xscale = xscalable ? xscale : 1.0,\r\n",
       "        yscale = yscalable ? yscale : 1.0;\r\n",
       "\r\n",
       "    tx = xscalable ? tx : 0.0;\r\n",
       "    ty = yscalable ? ty : 0.0;\r\n",
       "\r\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\r\n",
       "    root.selectAll(\".geometry, image\").forEach(function (element, i) {\r\n",
       "            element.transform(t);\r\n",
       "        });\r\n",
       "\r\n",
       "    var t = new Snap.Matrix().scale(1.0/xscale, 1.0/yscale);\r\n",
       "    root.selectAll('.marker').forEach(function (element, i) {\r\n",
       "        element.selectAll('.primitive').forEach(function (element, i) {\r\n",
       "            element.transform(t);\r\n",
       "        }) });\r\n",
       "\r\n",
       "    bounds = root.plotbounds();\r\n",
       "    px_per_mm = root.data(\"px_per_mm\");\r\n",
       "\r\n",
       "    if (yscalable) {\r\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\r\n",
       "        root.selectAll(\".xfixed\")\r\n",
       "            .forEach(function (element, i) {\r\n",
       "                element.transform(xfixed_t);\r\n",
       "            });\r\n",
       "\r\n",
       "        ylabels = root.select(\".ylabels\");\r\n",
       "        if (ylabels) {\r\n",
       "            ylabels.transform(xfixed_t)\r\n",
       "                   .selectAll(\"g\")\r\n",
       "                   .forEach(function (element, i) {\r\n",
       "                       if (element.attribute(\"gadfly:inscale\") == \"true\") {\r\n",
       "                           unscale_t = new Snap.Matrix();\r\n",
       "                           unscale_t.scale(1, 1/yscale);\r\n",
       "                           element.select(\"text\").transform(unscale_t);\r\n",
       " \r\n",
       "                           var y = element.attr(\"transform\").globalMatrix.f / px_per_mm;\r\n",
       "                           element.attr(\"visibility\",\r\n",
       "                               bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\r\n",
       "                       }\r\n",
       "                   });\r\n",
       "        }\r\n",
       "    }\r\n",
       "\r\n",
       "    if (xscalable) {\r\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\r\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\r\n",
       "        root.selectAll(\".yfixed\")\r\n",
       "            .forEach(function (element, i) {\r\n",
       "                element.transform(yfixed_t);\r\n",
       "            });\r\n",
       "\r\n",
       "        xlabels = root.select(\".xlabels\");\r\n",
       "        if (xlabels) {\r\n",
       "            xlabels.transform(yfixed_t)\r\n",
       "                   .selectAll(\"g\")\r\n",
       "                   .forEach(function (element, i) {\r\n",
       "                       if (element.attribute(\"gadfly:inscale\") == \"true\") {\r\n",
       "                           unscale_t = new Snap.Matrix();\r\n",
       "                           unscale_t.scale(1/xscale, 1);\r\n",
       "                           element.select(\"text\").transform(unscale_t);\r\n",
       "\r\n",
       "                           var x = element.attr(\"transform\").globalMatrix.e / px_per_mm;\r\n",
       "                           element.attr(\"visibility\",\r\n",
       "                               bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\r\n",
       "                           }\r\n",
       "                   });\r\n",
       "        }\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "// Find the most appropriate tick scale and update label visibility.\r\n",
       "var update_tickscale = function(root, scale, axis) {\r\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\r\n",
       "\r\n",
       "    var tickscales = root.data(axis + \"tickscales\");\r\n",
       "    var best_tickscale = 1.0;\r\n",
       "    var best_tickscale_dist = Infinity;\r\n",
       "    for (tickscale in tickscales) {\r\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\r\n",
       "        if (dist < best_tickscale_dist) {\r\n",
       "            best_tickscale_dist = dist;\r\n",
       "            best_tickscale = tickscale;\r\n",
       "        }\r\n",
       "    }\r\n",
       "\r\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\r\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\r\n",
       "        var mark_inscale_gridlines = function (element, i) {\r\n",
       "            if (element.attribute(\"gadfly:inscale\") == null) { return; }\r\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\r\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\r\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\r\n",
       "        };\r\n",
       "\r\n",
       "        var mark_inscale_labels = function (element, i) {\r\n",
       "            if (element.attribute(\"gadfly:inscale\") == null) { return; }\r\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\r\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\r\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\r\n",
       "        };\r\n",
       "\r\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"g\").forEach(mark_inscale_gridlines);\r\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"g\").forEach(mark_inscale_labels);\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "var set_plot_pan_zoom = function(root, tx, ty, xscale, yscale) {\r\n",
       "    var old_xscale = root.data(\"xscale\"),\r\n",
       "        old_yscale = root.data(\"yscale\");\r\n",
       "    var bounds = root.plotbounds();\r\n",
       "\r\n",
       "    var width = bounds.x1 - bounds.x0,\r\n",
       "        height = bounds.y1 - bounds.y0;\r\n",
       "\r\n",
       "    // compute the viewport derived from tx, ty, xscale, and yscale\r\n",
       "    var x_min = -width * xscale - (xscale * width - width),\r\n",
       "        x_max = width * xscale,\r\n",
       "        y_min = -height * yscale - (yscale * height - height),\r\n",
       "        y_max = height * yscale;\r\n",
       "\r\n",
       "    var x0 = bounds.x0 - xscale * bounds.x0,\r\n",
       "        y0 = bounds.y0 - yscale * bounds.y0;\r\n",
       "\r\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\r\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\r\n",
       "\r\n",
       "    tx += x0;\r\n",
       "    ty += y0;\r\n",
       "\r\n",
       "    // when the scale changes, we may need to alter which set of\r\n",
       "    // ticks are being displayed\r\n",
       "    if (xscale != old_xscale) {\r\n",
       "        update_tickscale(root, xscale, \"x\");\r\n",
       "    }\r\n",
       "    if (yscale != old_yscale) {\r\n",
       "        update_tickscale(root, yscale, \"y\");\r\n",
       "    }\r\n",
       "\r\n",
       "    set_geometry_transform(root, tx, ty, xscale, yscale);\r\n",
       "\r\n",
       "    root.data(\"xscale\", xscale);\r\n",
       "    root.data(\"yscale\", yscale);\r\n",
       "    root.data(\"tx\", tx);\r\n",
       "    root.data(\"ty\", ty);\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "var scale_centered_translation = function(root, xscale, yscale) {\r\n",
       "    var bounds = root.plotbounds();\r\n",
       "\r\n",
       "    var width = bounds.x1 - bounds.x0,\r\n",
       "        height = bounds.y1 - bounds.y0;\r\n",
       "\r\n",
       "    var tx0 = root.data(\"tx\"),\r\n",
       "        ty0 = root.data(\"ty\");\r\n",
       "\r\n",
       "    var xscale0 = root.data(\"xscale\"),\r\n",
       "        yscale0 = root.data(\"yscale\");\r\n",
       "\r\n",
       "    // how off from center the current view is\r\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - xscale0) + (width * (1 - xscale0)) / 2),\r\n",
       "        yoff = ty0 - (bounds.y0 * (1 - yscale0) + (height * (1 - yscale0)) / 2);\r\n",
       "\r\n",
       "    // rescale offsets\r\n",
       "    xoff = xoff * xscale / xscale0;\r\n",
       "    yoff = yoff * yscale / yscale0;\r\n",
       "\r\n",
       "    // adjust for the panel position being scaled\r\n",
       "    var x_edge_adjust = bounds.x0 * (1 - xscale),\r\n",
       "        y_edge_adjust = bounds.y0 * (1 - yscale);\r\n",
       "\r\n",
       "    return {\r\n",
       "        x: xoff + x_edge_adjust + (width - width * xscale) / 2,\r\n",
       "        y: yoff + y_edge_adjust + (height - height * yscale) / 2\r\n",
       "    };\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "// Initialize data for panning zooming if it isn't already.\r\n",
       "var init_pan_zoom = function(root) {\r\n",
       "    if (root.data(\"zoompan-ready\")) {\r\n",
       "        return;\r\n",
       "    }\r\n",
       "\r\n",
       "    root.data(\"crosshair\",false);\r\n",
       "\r\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\r\n",
       "    // stroke-width when zooming, we force it to a fixed value.\r\n",
       "    var px_per_mm = root.node.getCTM().a;\r\n",
       "\r\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\r\n",
       "    // millimeters.\r\n",
       "    root.data(\"px_per_mm\", px_per_mm);\r\n",
       "\r\n",
       "    root.selectAll(\"path\")\r\n",
       "        .forEach(function (element, i) {\r\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\r\n",
       "        if (sw > 0) {\r\n",
       "            element.attribute(\"stroke-width\", sw);\r\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\r\n",
       "        }\r\n",
       "    });\r\n",
       "\r\n",
       "    // Store ticks labels original tranformation\r\n",
       "    root.selectAll(\".xlabels > g, .ylabels > g\")\r\n",
       "        .forEach(function (element, i) {\r\n",
       "            var lm = element.transform().localMatrix;\r\n",
       "            element.data(\"static_transform\",\r\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\r\n",
       "        });\r\n",
       "\r\n",
       "    var xgridlines = root.select(\".xgridlines\");\r\n",
       "    var ygridlines = root.select(\".ygridlines\");\r\n",
       "    var xlabels = root.select(\".xlabels\");\r\n",
       "    var ylabels = root.select(\".ylabels\");\r\n",
       "\r\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\r\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\r\n",
       "    if (root.data(\"xscale\") === undefined) root.data(\"xscale\", 1.0);\r\n",
       "    if (root.data(\"yscale\") === undefined) root.data(\"yscale\", 1.0);\r\n",
       "    if (root.data(\"xtickscales\") === undefined) {\r\n",
       "\r\n",
       "        // index all the tick scales that are listed\r\n",
       "        var xtickscales = {};\r\n",
       "        var ytickscales = {};\r\n",
       "        var add_x_tick_scales = function (element, i) {\r\n",
       "            if (element.attribute(\"gadfly:scale\")==null) { return; }\r\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\r\n",
       "        };\r\n",
       "        var add_y_tick_scales = function (element, i) {\r\n",
       "            if (element.attribute(\"gadfly:scale\")==null) { return; }\r\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\r\n",
       "        };\r\n",
       "\r\n",
       "        if (xgridlines) xgridlines.selectAll(\"g\").forEach(add_x_tick_scales);\r\n",
       "        if (ygridlines) ygridlines.selectAll(\"g\").forEach(add_y_tick_scales);\r\n",
       "        if (xlabels) xlabels.selectAll(\"g\").forEach(add_x_tick_scales);\r\n",
       "        if (ylabels) ylabels.selectAll(\"g\").forEach(add_y_tick_scales);\r\n",
       "\r\n",
       "        root.data(\"xtickscales\", xtickscales);\r\n",
       "        root.data(\"ytickscales\", ytickscales);\r\n",
       "        root.data(\"xtickscale\", 1.0);\r\n",
       "        root.data(\"ytickscale\", 1.0);  // ???\r\n",
       "    }\r\n",
       "\r\n",
       "    var min_scale = 1.0, max_scale = 1.0;\r\n",
       "    for (scale in xtickscales) {\r\n",
       "        min_scale = Math.min(min_scale, scale);\r\n",
       "        max_scale = Math.max(max_scale, scale);\r\n",
       "    }\r\n",
       "    for (scale in ytickscales) {\r\n",
       "        min_scale = Math.min(min_scale, scale);\r\n",
       "        max_scale = Math.max(max_scale, scale);\r\n",
       "    }\r\n",
       "    root.data(\"min_scale\", min_scale);\r\n",
       "    root.data(\"max_scale\", max_scale);\r\n",
       "\r\n",
       "    // store the original positions of labels\r\n",
       "    if (xlabels) {\r\n",
       "        xlabels.selectAll(\"g\")\r\n",
       "               .forEach(function (element, i) {\r\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\r\n",
       "               });\r\n",
       "    }\r\n",
       "\r\n",
       "    if (ylabels) {\r\n",
       "        ylabels.selectAll(\"g\")\r\n",
       "               .forEach(function (element, i) {\r\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\r\n",
       "               });\r\n",
       "    }\r\n",
       "\r\n",
       "    // mark grid lines and ticks as in or out of scale.\r\n",
       "    var mark_inscale = function (element, i) {\r\n",
       "        if (element.attribute(\"gadfly:scale\") == null) { return; }\r\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\r\n",
       "    };\r\n",
       "\r\n",
       "    if (xgridlines) xgridlines.selectAll(\"g\").forEach(mark_inscale);\r\n",
       "    if (ygridlines) ygridlines.selectAll(\"g\").forEach(mark_inscale);\r\n",
       "    if (xlabels) xlabels.selectAll(\"g\").forEach(mark_inscale);\r\n",
       "    if (ylabels) ylabels.selectAll(\"g\").forEach(mark_inscale);\r\n",
       "\r\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\r\n",
       "    // and minum grid lines\r\n",
       "    var bounds = root.plotbounds();\r\n",
       "    var pan_bounds = {\r\n",
       "        x0: 0.0,\r\n",
       "        y0: 0.0,\r\n",
       "        x1: 0.0,\r\n",
       "        y1: 0.0\r\n",
       "    };\r\n",
       "\r\n",
       "    if (xgridlines) {\r\n",
       "        xgridlines\r\n",
       "            .selectAll(\"g\")\r\n",
       "            .forEach(function (element, i) {\r\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\r\n",
       "                    var bbox = element.node.getBBox();\r\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\r\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\r\n",
       "                    }\r\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\r\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\r\n",
       "                    }\r\n",
       "                    element.attr(\"visibility\", \"visible\");\r\n",
       "                }\r\n",
       "            });\r\n",
       "    }\r\n",
       "\r\n",
       "    if (ygridlines) {\r\n",
       "        ygridlines\r\n",
       "            .selectAll(\"g\")\r\n",
       "            .forEach(function (element, i) {\r\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\r\n",
       "                    var bbox = element.node.getBBox();\r\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\r\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\r\n",
       "                    }\r\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\r\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\r\n",
       "                    }\r\n",
       "                    element.attr(\"visibility\", \"visible\");\r\n",
       "                }\r\n",
       "            });\r\n",
       "    }\r\n",
       "\r\n",
       "    // nudge these values a little\r\n",
       "    pan_bounds.x0 -= 5;\r\n",
       "    pan_bounds.x1 += 5;\r\n",
       "    pan_bounds.y0 -= 5;\r\n",
       "    pan_bounds.y1 += 5;\r\n",
       "    root.data(\"pan_bounds\", pan_bounds);\r\n",
       "\r\n",
       "    root.data(\"zoompan-ready\", true)\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "// drag actions, i.e. zooming and panning\r\n",
       "var pan_action = {\r\n",
       "    start: function(root, x, y, event) {\r\n",
       "        root.data(\"dx\", 0);\r\n",
       "        root.data(\"dy\", 0);\r\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\r\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\r\n",
       "    },\r\n",
       "    update: function(root, dx, dy, x, y, event) {\r\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\r\n",
       "        dx /= px_per_mm;\r\n",
       "        dy /= px_per_mm;\r\n",
       "\r\n",
       "        var tx0 = root.data(\"tx\"),\r\n",
       "            ty0 = root.data(\"ty\");\r\n",
       "\r\n",
       "        var dx0 = root.data(\"dx\"),\r\n",
       "            dy0 = root.data(\"dy\");\r\n",
       "\r\n",
       "        root.data(\"dx\", dx);\r\n",
       "        root.data(\"dy\", dy);\r\n",
       "\r\n",
       "        dx = dx - dx0;\r\n",
       "        dy = dy - dy0;\r\n",
       "\r\n",
       "        var tx = tx0 + dx,\r\n",
       "            ty = ty0 + dy;\r\n",
       "\r\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"xscale\"), root.data(\"yscale\"));\r\n",
       "    },\r\n",
       "    end: function(root, event) {\r\n",
       "\r\n",
       "    },\r\n",
       "    cancel: function(root) {\r\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"),\r\n",
       "                root.data(\"xscale\"), root.data(\"yscale\"));\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "var zoom_box;\r\n",
       "var zoom_action = {\r\n",
       "    start: function(root, x, y, event) {\r\n",
       "        var bounds = root.plotbounds();\r\n",
       "        var width = bounds.x1 - bounds.x0,\r\n",
       "            height = bounds.y1 - bounds.y0;\r\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\r\n",
       "            yscalable = root.hasClass(\"yscalable\");\r\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\r\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\r\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\r\n",
       "        var w = xscalable ? 0 : width;\r\n",
       "        var h = yscalable ? 0 : height;\r\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\r\n",
       "            \"fill\": \"#000\",\r\n",
       "            \"opacity\": 0.25\r\n",
       "        });\r\n",
       "    },\r\n",
       "    update: function(root, dx, dy, x, y, event) {\r\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\r\n",
       "            yscalable = root.hasClass(\"yscalable\");\r\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\r\n",
       "        var bounds = root.plotbounds();\r\n",
       "        if (yscalable) {\r\n",
       "            y /= px_per_mm;\r\n",
       "            y = Math.max(bounds.y0, y);\r\n",
       "            y = Math.min(bounds.y1, y);\r\n",
       "        } else {\r\n",
       "            y = bounds.y1;\r\n",
       "        }\r\n",
       "        if (xscalable) {\r\n",
       "            x /= px_per_mm;\r\n",
       "            x = Math.max(bounds.x0, x);\r\n",
       "            x = Math.min(bounds.x1, x);\r\n",
       "        } else {\r\n",
       "            x = bounds.x1;\r\n",
       "        }\r\n",
       "\r\n",
       "        dx = x - zoom_box.attr(\"x\");\r\n",
       "        dy = y - zoom_box.attr(\"y\");\r\n",
       "        var xoffset = 0,\r\n",
       "            yoffset = 0;\r\n",
       "        if (dx < 0) {\r\n",
       "            xoffset = dx;\r\n",
       "            dx = -1 * dx;\r\n",
       "        }\r\n",
       "        if (dy < 0) {\r\n",
       "            yoffset = dy;\r\n",
       "            dy = -1 * dy;\r\n",
       "        }\r\n",
       "        if (isNaN(dy)) {\r\n",
       "            dy = 0.0;\r\n",
       "        }\r\n",
       "        if (isNaN(dx)) {\r\n",
       "            dx = 0.0;\r\n",
       "        }\r\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\r\n",
       "        zoom_box.attr(\"width\", dx);\r\n",
       "        zoom_box.attr(\"height\", dy);\r\n",
       "    },\r\n",
       "    end: function(root, event) {\r\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\r\n",
       "            yscalable = root.hasClass(\"yscalable\");\r\n",
       "        var zoom_bounds = zoom_box.getBBox();\r\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\r\n",
       "            return;\r\n",
       "        }\r\n",
       "        var plot_bounds = root.plotbounds();\r\n",
       "        var xzoom_factor = 1.0,\r\n",
       "            yzoom_factor = 1.0;\r\n",
       "        if (xscalable) {\r\n",
       "            xzoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\r\n",
       "        }\r\n",
       "        if (yscalable) {\r\n",
       "            yzoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\r\n",
       "        }\r\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * xzoom_factor + plot_bounds.x0,\r\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * yzoom_factor + plot_bounds.y0;\r\n",
       "        set_plot_pan_zoom(root, tx, ty,\r\n",
       "                root.data(\"xscale\") * xzoom_factor, root.data(\"yscale\") * yzoom_factor);\r\n",
       "        zoom_box.remove();\r\n",
       "    },\r\n",
       "    cancel: function(root) {\r\n",
       "        zoom_box.remove();\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\r\n",
       "    var root = this.plotroot();\r\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\r\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\r\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\r\n",
       "    var drag_action = zoomable ? zoom_action :\r\n",
       "                      panable  ? pan_action :\r\n",
       "                                 undefined;\r\n",
       "    root.data(\"drag_action\", drag_action);\r\n",
       "    if (drag_action) {\r\n",
       "        var cancel_drag_action = function(event) {\r\n",
       "            if (event.which == 27) { // esc key\r\n",
       "                drag_action.cancel(root);\r\n",
       "                root.data(\"drag_action\", undefined);\r\n",
       "            }\r\n",
       "        };\r\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\r\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\r\n",
       "        drag_action.start(root, x, y, event);\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\r\n",
       "    var root = this.plotroot();\r\n",
       "    var drag_action = root.data(\"drag_action\");\r\n",
       "    if (drag_action) {\r\n",
       "        drag_action.update(root, dx, dy, x, y, event);\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "Gadfly.guide_background_drag_onend = function(event) {\r\n",
       "    var root = this.plotroot();\r\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\r\n",
       "    root.data(\"cancel_drag_action\", undefined);\r\n",
       "    var drag_action = root.data(\"drag_action\");\r\n",
       "    if (drag_action) {\r\n",
       "        drag_action.end(root, event);\r\n",
       "    }\r\n",
       "    root.data(\"drag_action\", undefined);\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "Gadfly.guide_background_scroll = function(event) {\r\n",
       "    if (event.shiftKey) {\r\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\r\n",
       "        event.preventDefault();\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\r\n",
       "// The constants a, b, and c are solved using the constraint that the function\r\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\r\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\r\n",
       "    if (min_scale==max_scale) { return 1; }\r\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\r\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\r\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\r\n",
       "    return a * Math.exp(b * position) + c;\r\n",
       "}\r\n",
       "\r\n",
       "// inverse of scale_from_slider_position\r\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\r\n",
       "    if (min_scale==max_scale) { return min_scale; }\r\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\r\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\r\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\r\n",
       "    return 1 / b * Math.log((scale - c) / a);\r\n",
       "}\r\n",
       "\r\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\r\n",
       "    var old_xscale = root.data(\"xscale\"),\r\n",
       "        old_yscale = root.data(\"yscale\"),\r\n",
       "        min_scale = root.data(\"min_scale\"),\r\n",
       "        max_scale = root.data(\"max_scale\");\r\n",
       "    var xposition = slider_position_from_scale(old_xscale, min_scale, max_scale),\r\n",
       "        yposition = slider_position_from_scale(old_yscale, min_scale, max_scale);\r\n",
       "    xposition += (root.hasClass(\"xscalable\") ? delta_position : 0.0);\r\n",
       "    yposition += (root.hasClass(\"yscalable\") ? delta_position : 0.0);\r\n",
       "    old_xscale = scale_from_slider_position(xposition, min_scale, max_scale);\r\n",
       "    old_yscale = scale_from_slider_position(yposition, min_scale, max_scale);\r\n",
       "    var new_xscale = Math.max(min_scale, Math.min(old_xscale, max_scale)),\r\n",
       "        new_yscale = Math.max(min_scale, Math.min(old_yscale, max_scale));\r\n",
       "    if (animate) {\r\n",
       "        Snap.animate(\r\n",
       "            [old_xscale, old_yscale],\r\n",
       "            [new_xscale, new_yscale],\r\n",
       "            function (new_scale) {\r\n",
       "                update_plot_scale(root, new_scale[0], new_scale[1]);\r\n",
       "            },\r\n",
       "            200);\r\n",
       "    } else {\r\n",
       "        update_plot_scale(root, new_xscale, new_yscale);\r\n",
       "    }\r\n",
       "}\r\n",
       "\r\n",
       "\r\n",
       "var update_plot_scale = function(root, new_xscale, new_yscale) {\r\n",
       "    var trans = scale_centered_translation(root, new_xscale, new_yscale);\r\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_xscale, new_yscale);\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "var toggle_color_class = function(root, color_class, ison) {\r\n",
       "    var escaped_color_class = color_class.replace(/([^0-9a-zA-z])/g,\"\\\\$1\");\r\n",
       "    var guides = root.selectAll(\".guide.\" + escaped_color_class + \",.guide .\" + escaped_color_class);\r\n",
       "    var geoms = root.selectAll(\".geometry.\" + escaped_color_class + \",.geometry .\" + escaped_color_class);\r\n",
       "    if (ison) {\r\n",
       "        guides.animate({opacity: 0.5}, 250);\r\n",
       "        geoms.animate({opacity: 0.0}, 250);\r\n",
       "    } else {\r\n",
       "        guides.animate({opacity: 1.0}, 250);\r\n",
       "        geoms.animate({opacity: 1.0}, 250);\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "Gadfly.colorkey_swatch_click = function(event) {\r\n",
       "    var root = this.plotroot();\r\n",
       "    var color_class = this.data(\"color_class\");\r\n",
       "\r\n",
       "    if (event.shiftKey) {\r\n",
       "        root.selectAll(\".colorkey g\")\r\n",
       "            .forEach(function (element) {\r\n",
       "                var other_color_class = element.data(\"color_class\");\r\n",
       "                if (typeof other_color_class !== 'undefined' && other_color_class != color_class) {\r\n",
       "                    toggle_color_class(root, other_color_class,\r\n",
       "                                       element.attr(\"opacity\") == 1.0);\r\n",
       "                }\r\n",
       "            });\r\n",
       "    } else {\r\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\r\n",
       "    }\r\n",
       "};\r\n",
       "\r\n",
       "\r\n",
       "return Gadfly;\r\n",
       "\r\n",
       "}));\r\n",
       "\r\n",
       "\r\n",
       "//@ sourceURL=gadfly.js\r\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-130e4328\");\n",
       "fig.select(\"#img-130e4328-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#img-130e4328-6\")\n",
       "   .data(\"color_class\", \"color_tabby,_tabby_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-7\")\n",
       "   .data(\"color_class\", \"color_tiger_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-8\")\n",
       "   .data(\"color_class\", \"color_lynx,_catamount\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-9\")\n",
       "   .data(\"color_class\", \"color_Egyptian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-10\")\n",
       "   .data(\"color_class\", \"color_Persian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-12\")\n",
       "   .data(\"color_class\", \"color_tabby,_tabby_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-13\")\n",
       "   .data(\"color_class\", \"color_tiger_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-14\")\n",
       "   .data(\"color_class\", \"color_lynx,_catamount\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-15\")\n",
       "   .data(\"color_class\", \"color_Egyptian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-16\")\n",
       "   .data(\"color_class\", \"color_Persian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#img-130e4328-20\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-130e4328-23\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-130e4328-23\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-130e4328-118\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-130e4328-118\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-130e4328-294\")\n",
       "   .mouseenter(Gadfly.helpscreen_visible)\n",
       ".mouseleave(Gadfly.helpscreen_hidden)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Gadfly\n",
    "\n",
    "n_plot = 5\n",
    "n_best = sortperm(vec(prob), rev=true)[1:n_plot]\n",
    "best_probs = prob[n_best]\n",
    "best_labels = classes[n_best]\n",
    "\n",
    "plot(x=1:length(best_probs), y=best_probs, color=best_labels, Geom.bar, Guide.ylabel(\"probability\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the predicted class probability is highly concentrated. Here is another example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "256×256 Array{RGB{N0f8},2}:\n",
       " RGB{N0f8}(0.592,0.439,0.137)  …  RGB{N0f8}(0.357,0.369,0.02) \n",
       " RGB{N0f8}(0.592,0.435,0.153)     RGB{N0f8}(0.361,0.38,0.02)  \n",
       " RGB{N0f8}(0.604,0.447,0.165)     RGB{N0f8}(0.373,0.392,0.024)\n",
       " RGB{N0f8}(0.624,0.463,0.149)     RGB{N0f8}(0.376,0.392,0.059)\n",
       " RGB{N0f8}(0.639,0.475,0.145)     RGB{N0f8}(0.376,0.388,0.071)\n",
       " RGB{N0f8}(0.643,0.482,0.169)  …  RGB{N0f8}(0.376,0.388,0.086)\n",
       " RGB{N0f8}(0.651,0.49,0.176)      RGB{N0f8}(0.38,0.38,0.106)  \n",
       " RGB{N0f8}(0.671,0.498,0.157)     RGB{N0f8}(0.392,0.384,0.094)\n",
       " RGB{N0f8}(0.671,0.514,0.169)     RGB{N0f8}(0.388,0.38,0.09)  \n",
       " RGB{N0f8}(0.686,0.51,0.176)      RGB{N0f8}(0.38,0.38,0.09)   \n",
       " RGB{N0f8}(0.69,0.522,0.161)   …  RGB{N0f8}(0.38,0.376,0.063) \n",
       " RGB{N0f8}(0.706,0.533,0.153)     RGB{N0f8}(0.396,0.376,0.055)\n",
       " RGB{N0f8}(0.722,0.525,0.153)     RGB{N0f8}(0.396,0.369,0.063)\n",
       " ⋮                             ⋱  ⋮                           \n",
       " RGB{N0f8}(0.42,0.341,0.063)      RGB{N0f8}(0.8,0.561,0.004)  \n",
       " RGB{N0f8}(0.42,0.341,0.071)   …  RGB{N0f8}(0.8,0.561,0.004)  \n",
       " RGB{N0f8}(0.431,0.337,0.055)     RGB{N0f8}(0.8,0.561,0.004)  \n",
       " RGB{N0f8}(0.435,0.337,0.075)     RGB{N0f8}(0.792,0.553,0.004)\n",
       " RGB{N0f8}(0.435,0.341,0.106)     RGB{N0f8}(0.788,0.549,0.0)  \n",
       " RGB{N0f8}(0.447,0.329,0.094)     RGB{N0f8}(0.78,0.549,0.0)   \n",
       " RGB{N0f8}(0.455,0.329,0.09)   …  RGB{N0f8}(0.788,0.545,0.016)\n",
       " RGB{N0f8}(0.451,0.333,0.09)      RGB{N0f8}(0.784,0.549,0.039)\n",
       " RGB{N0f8}(0.455,0.329,0.09)      RGB{N0f8}(0.776,0.537,0.039)\n",
       " RGB{N0f8}(0.49,0.325,0.098)      RGB{N0f8}(0.765,0.533,0.024)\n",
       " RGB{N0f8}(0.498,0.322,0.098)     RGB{N0f8}(0.757,0.529,0.035)\n",
       " RGB{N0f8}(0.482,0.333,0.09)   …  RGB{N0f8}(0.761,0.525,0.055)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img2 = FileIO.load(\"images/bird.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indigo bunting, indigo finch, indigo bird, Passerina cyanea\n"
     ]
    }
   ],
   "source": [
    "prob, class = classify(classifier, img2)\n",
    "println(class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.6.1",
   "language": "julia",
   "name": "julia-0.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
